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of Contents"],"sub_categories":[],"readme":"# graph4nlp_literature\nThis repo is to provide a list of literature regarding Deep Learning on Graphs for NLP\n\n\n- ## Dialogue Generation\n\t- ### GNN for Directed Graphs\n\t\t* #### IJCAI-19\n\t\t\t[GSN: A Graph-Structured Network for Multi-Party Dialogues](https://www.ijcai.org/Proceedings/2019/696) \n\n \t- ### App-driven\n\t\t* #### IJCAI-19\n\t\t\t[GSN: A Graph-Structured Network for Multi-Party Dialogues](https://www.ijcai.org/Proceedings/2019/696) \n\n - ## Open-domain Question Answering\n\t- ### GNN for Directed Graphs\n\t\t* #### EMNLP-19\n\t\t\t[PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://www.aclweb.org/anthology/D19-1242/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion](https://www.aclweb.org/anthology/2020.findings-emnlp.133/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://www.aclweb.org/anthology/D18-1455/) \n\n \t- ### Graph2Seq\n\t\t* #### EMNLP-19\n\t\t\t[PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://www.aclweb.org/anthology/D19-1242/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion](https://www.aclweb.org/anthology/2020.findings-emnlp.133/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://www.aclweb.org/anthology/D18-1455/) \n\n \t- ### Knowledge\n\t\t* #### EMNLP-19\n\t\t\t[PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://www.aclweb.org/anthology/D19-1242/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion](https://www.aclweb.org/anthology/2020.findings-emnlp.133/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://www.aclweb.org/anthology/D18-1455/) \n\n - ## Commonsense\n\t- ### Knowledge\n\t\t* #### IJCAI-18\n\t\t\t[Commonsense Knowledge Aware Conversation Generation with Graph Attention](https://www.ijcai.org/Proceedings/2018/643) \n\n - ## Knowledge Graph Alignment\n\t- ### GNN for Directed Graphs\n\t\t* #### ACL-19\n\t\t\t[Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network](https://www.aclweb.org/anthology/P19-1304.pdf) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ACL-19\n\t\t\t[Multi-Channel Graph Neural Network for Entity Alignment](https://www.aclweb.org/anthology/P19-1140.pdf) \n\n \t\t* #### IJCAI-19\n\t\t\t[A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment](https://www.ijcai.org/Proceedings/2019/0574.pdf) \n\n \t\t\t[Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs](https://www.ijcai.org/Proceedings/2019/0733.pdf) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### AAAI-20\n\t\t\t[Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation](https://ojs.aaai.org//index.php/AAAI/article/view/5354) \n\n \t\t\t[Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment](https://ojs.aaai.org//index.php/AAAI/article/view/6476) \n\n \t\t* #### EMNLP-19\n\t\t\t[Aligning Cross-Lingual Entities with Multi-Aspect Information](https://www.aclweb.org/anthology/D19-1451/) \n\n \t\t\t[Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model](https://www.aclweb.org/anthology/D19-1274.pdf) \n\n \t\t\t[Jointly Learning Entity and Relation Representations for Entity Alignment](https://www.aclweb.org/anthology/D19-1023/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Knowledge Graph Alignment with Entity-Pair Embedding](https://www.aclweb.org/anthology/2020.emnlp-main.130.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks](https://www.aclweb.org/anthology/D18-1032/) \n\n \t- ### Knowledge\n\t\t* #### AAAI-20\n\t\t\t[Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation](https://ojs.aaai.org//index.php/AAAI/article/view/5354) \n\n \t\t\t[Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment](https://ojs.aaai.org//index.php/AAAI/article/view/6476) \n\n \t\t* #### ACL-19\n\t\t\t[Multi-Channel Graph Neural Network for Entity Alignment](https://www.aclweb.org/anthology/P19-1140.pdf) \n\n \t\t\t[Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network](https://www.aclweb.org/anthology/P19-1304.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[Aligning Cross-Lingual Entities with Multi-Aspect Information](https://www.aclweb.org/anthology/D19-1451/) \n\n \t\t\t[Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model](https://www.aclweb.org/anthology/D19-1274.pdf) \n\n \t\t\t[Jointly Learning Entity and Relation Representations for Entity Alignment](https://www.aclweb.org/anthology/D19-1023/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Knowledge Graph Alignment with Entity-Pair Embedding](https://www.aclweb.org/anthology/2020.emnlp-main.130.pdf) \n\n \t\t* #### IJCAI-19\n\t\t\t[A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment](https://www.ijcai.org/Proceedings/2019/0574.pdf) \n\n \t\t\t[Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs](https://www.ijcai.org/Proceedings/2019/0733.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks](https://www.aclweb.org/anthology/D18-1032/) \n\n - ## Named-entity Recognition\n\t- ### Node Embedding Based Refined\n\t\t* #### NAACL-19\n\t\t\t[A General Framework for Information Extraction using Dynamic Span Graphs](https://www.aclweb.org/anthology/N19-1308.pdf) \n\n \t- ### Coreference\n\t\t* #### NAACL-19\n\t\t\t[A General Framework for Information Extraction using Dynamic Span Graphs](https://www.aclweb.org/anthology/N19-1308.pdf) \n\n \t- ### Dependency\n\t\t* #### ACL-19\n\t\t\t[GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction](https://www.aclweb.org/anthology/P19-1136.pdf) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ACL-20\n\t\t\t[Bipartite Flat-Graph Network for Nested Named Entity Recognition](https://www.aclweb.org/anthology/2020.acl-main.571.pdf) \n\n \t\t* #### ACL-19\n\t\t\t[A Neural Multi-digraph Model for Chinese NER with Gazetteers](https://www.aclweb.org/anthology/P19-1141.pdf) \n\n \t- ### App-driven\n\t\t* #### ACL-20\n\t\t\t[Bipartite Flat-Graph Network for Nested Named Entity Recognition](https://www.aclweb.org/anthology/2020.acl-main.571.pdf) \n\n \t\t* #### ACL-19\n\t\t\t[A Neural Multi-digraph Model for Chinese NER with Gazetteers](https://www.aclweb.org/anthology/P19-1141.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[A Lexicon-Based Graph Neural Network for Chinese NER](https://www.aclweb.org/anthology/D19-1096.pdf) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ACL-19\n\t\t\t[GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction](https://www.aclweb.org/anthology/P19-1136.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[A Lexicon-Based Graph Neural Network for Chinese NER](https://www.aclweb.org/anthology/D19-1096.pdf) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### EMNLP-19\n\t\t\t[Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network](https://www.aclweb.org/anthology/D19-1396/) \n\n \t- ### Knowledge\n\t\t* #### EMNLP-19\n\t\t\t[Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network](https://www.aclweb.org/anthology/D19-1396/) \n\n - ## AMR2Text\n\t- ### Graph2Graph\n\t\t* #### EMNLP-20\n\t\t\t[Online Back-Parsing for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.92/) \n\n \t- ### Dependency\n\t\t* #### ACL-18\n\t\t\t[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://www.aclweb.org/anthology/P18-1026/) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### AAAI-20\n\t\t\t[Graph Transformer for Graph-to-Sequence Learning](https://ojs.aaai.org//index.php/AAAI/article/view/6243) \n\n \t\t* #### COLING-20\n\t\t\t[Generalized Shortest-Paths Encoders for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.coling-main.181.pdf) \n\n \t\t* #### ACL-20\n\t\t\t[Structural Information Preserving for Graph-to-Text Generation](https://www.aclweb.org/anthology/2020.acl-main.712/) \n\n \t\t\t[Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks](https://www.aclweb.org/anthology/2020.acl-main.67/) \n\n \t\t* #### ACL-19\n\t\t\t[Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://www.aclweb.org/anthology/D19-1548/) \n\n \t\t* #### TACL-20\n\t\t\t[AMR-To-Text Generation with Graph Transformer](https://www.aclweb.org/anthology/2020.tacl-1.2.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[Enhancing AMR-to-Text Generation with Dual Graph Representations](https://www.aclweb.org/anthology/D19-1314/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Online Back-Parsing for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.92/) \n\n \t\t\t[Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.169/) \n\n \t\t* #### ACL-18\n\t\t\t[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://www.aclweb.org/anthology/P18-1026/) \n\n \t\t\t[A Graph-to-Sequence Model for AMR-to-Text Generation](https://www.aclweb.org/anthology/P18-1150/) \n\n \t\t* #### NAACL-19\n\t\t\t[Structural Neural Encoders for AMR-to-text Generation](https://www.aclweb.org/anthology/N19-1366/) \n\n \t\t* #### IJCAI-20\n\t\t\t[Better AMR-To-Text Generation with Graph Structure Reconstruction](https://www.ijcai.org/Proceedings/2020/0542.pdf) \n\n \t\t* #### TACL-19\n\t\t\t[Semantic neural machine translation using AMR](https://www.aclweb.org/anthology/Q19-1002.pdf) \n\n \t- ### AMR\n\t\t* #### AAAI-20\n\t\t\t[Graph Transformer for Graph-to-Sequence Learning](https://ojs.aaai.org//index.php/AAAI/article/view/6243) \n\n \t\t* #### COLING-20\n\t\t\t[Generalized Shortest-Paths Encoders for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.coling-main.181.pdf) \n\n \t\t* #### ACL-20\n\t\t\t[Heterogeneous Graph Transformer for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/2020.acl-main.640.pdf) \n\n \t\t\t[Structural Information Preserving for Graph-to-Text Generation](https://www.aclweb.org/anthology/2020.acl-main.712/) \n\n \t\t\t[Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks](https://www.aclweb.org/anthology/2020.acl-main.67/) \n\n \t\t* #### ACL-19\n\t\t\t[Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://www.aclweb.org/anthology/D19-1548/) \n\n \t\t* #### TACL-20\n\t\t\t[AMR-To-Text Generation with Graph Transformer](https://www.aclweb.org/anthology/2020.tacl-1.2.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[Enhancing AMR-to-Text Generation with Dual Graph Representations](https://www.aclweb.org/anthology/D19-1314/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Online Back-Parsing for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.92/) \n\n \t\t\t[Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.169/) \n\n \t\t* #### ACL-18\n\t\t\t[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://www.aclweb.org/anthology/P18-1026/) \n\n \t\t\t[A Graph-to-Sequence Model for AMR-to-Text Generation](https://www.aclweb.org/anthology/P18-1150/) \n\n \t\t* #### NAACL-19\n\t\t\t[Structural Neural Encoders for AMR-to-text Generation](https://www.aclweb.org/anthology/N19-1366/) \n\n \t\t* #### IJCAI-20\n\t\t\t[Better AMR-To-Text Generation with Graph Structure Reconstruction](https://www.ijcai.org/Proceedings/2020/0542.pdf) \n\n \t\t* #### TACL-19\n\t\t\t[Semantic neural machine translation using AMR](https://www.aclweb.org/anthology/Q19-1002.pdf) \n\n \t\t\t[Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/Q19-1019/) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ACL-20\n\t\t\t[Heterogeneous Graph Transformer for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/2020.acl-main.640.pdf) \n\n \t- ### Graph2Seq\n\t\t* #### AAAI-20\n\t\t\t[Graph Transformer for Graph-to-Sequence Learning](https://ojs.aaai.org//index.php/AAAI/article/view/6243) \n\n \t\t* #### COLING-20\n\t\t\t[Generalized Shortest-Paths Encoders for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.coling-main.181.pdf) \n\n \t\t* #### ACL-20\n\t\t\t[Heterogeneous Graph Transformer for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/2020.acl-main.640.pdf) \n\n \t\t\t[Structural Information Preserving for Graph-to-Text Generation](https://www.aclweb.org/anthology/2020.acl-main.712/) \n\n \t\t\t[Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks](https://www.aclweb.org/anthology/2020.acl-main.67/) \n\n \t\t* #### ACL-19\n\t\t\t[Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://www.aclweb.org/anthology/D19-1548/) \n\n \t\t* #### TACL-20\n\t\t\t[AMR-To-Text Generation with Graph Transformer](https://www.aclweb.org/anthology/2020.tacl-1.2.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[Enhancing AMR-to-Text Generation with Dual Graph Representations](https://www.aclweb.org/anthology/D19-1314/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Online Back-Parsing for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.92/) \n\n \t\t\t[Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.169/) \n\n \t\t* #### ACL-18\n\t\t\t[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://www.aclweb.org/anthology/P18-1026/) \n\n \t\t\t[A Graph-to-Sequence Model for AMR-to-Text Generation](https://www.aclweb.org/anthology/P18-1150/) \n\n \t\t* #### NAACL-19\n\t\t\t[Structural Neural Encoders for AMR-to-text Generation](https://www.aclweb.org/anthology/N19-1366/) \n\n \t\t* #### IJCAI-20\n\t\t\t[Better AMR-To-Text Generation with Graph Structure Reconstruction](https://www.ijcai.org/Proceedings/2020/0542.pdf) \n\n \t\t* #### TACL-19\n\t\t\t[Semantic neural machine translation using AMR](https://www.aclweb.org/anthology/Q19-1002.pdf) \n\n \t\t\t[Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/Q19-1019/) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### TACL-19\n\t\t\t[Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/Q19-1019/) \n\n \t- ### Knowledge\n\t\t* #### ACL-20\n\t\t\t[Structural Information Preserving for Graph-to-Text Generation](https://www.aclweb.org/anthology/2020.acl-main.712/) \n\n - ## Math Word Problem\n\t- ### Dependency\n\t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n \t- ### App-driven\n\t\t* #### ACL-20\n\t\t\t[Graph-to-Tree Learning for Solving Math Word Problems](https://www.aclweb.org/anthology/2020.acl-main.362/) \n\n \t\t\t[Premise Selection in Natural Language Mathematical Texts](https://www.aclweb.org/anthology/2020.acl-main.657/) \n\n \t\t* #### ICLR-20\n\t\t\t[Mathematical Reasoning in Latent Space](https://openreview.net/forum?id=Ske31kBtPr) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n \t- ### Constituency\n\t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n \t- ### Knowledge\n\t\t* #### ACL-20\n\t\t\t[A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving](https://www.aclweb.org/anthology/2020.emnlp-main.579/) \n\n \t- ### Graph2Tree\n\t\t* #### ACL-20\n\t\t\t[A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving](https://www.aclweb.org/anthology/2020.emnlp-main.579/) \n\n \t\t\t[Graph-to-Tree Learning for Solving Math Word Problems](https://www.aclweb.org/anthology/2020.acl-main.362/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n - ## Topic Modeling\n\t- ### Co-occurrence\n\t\t* #### EMNLP-18\n\t\t\t[GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model](https://www.aclweb.org/anthology/D18-1495) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### WWW-20\n\t\t\t[Graph Attention Topic Modeling Network](http://doi.org/10.1145/3366423.3380102) \n\n \t\t* #### EMNLP-18\n\t\t\t[GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model](https://www.aclweb.org/anthology/D18-1495) \n\n \t- ### GNN on Directed Graphs\n\t\t* #### EMNLP-20\n\t\t\t[Neural Topic Modeling by Incorporating Document Relationship Graph](https://www.aclweb.org/anthology/2020.emnlp-main.310) \n\n \t- ### Topic\n\t\t* #### KDD-20\n\t\t\t[Graph Structural-topic Neural Network](http://doi.org/10.1145/3394486.3403150) \n\n \t\t* #### ACL-20\n\t\t\t[Tree-Structured Neural Topic Model](https://www.aclweb.org/anthology/2020.acl-main.73/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Neural Topic Modeling by Incorporating Document Relationship Graph](https://www.aclweb.org/anthology/2020.emnlp-main.310) \n\n - ## Dialogue State Tracking\n\t- ### GNN for Directed Graphs\n\t\t* #### COLING-19\n\t\t\t[Structured Dialogue Policy with Graph Neural Networks](https://www.aclweb.org/anthology/C18-1107/) \n\n \t- ### App-driven\n\t\t* #### AAAI-20\n\t\t\t[Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks](https://ojs.aaai.org//index.php/AAAI/article/view/6250) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### AAAI-20\n\t\t\t[Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks](https://ojs.aaai.org//index.php/AAAI/article/view/6250) \n\n \t- ### Node Embedding Based\n\t\t* #### COLING-19\n\t\t\t[Structured Dialogue Policy with Graph Neural Networks](https://www.aclweb.org/anthology/C18-1107/) \n\n - ## Parsing\n\t- ### GNN for Directed Graphs\n\t\t* #### NeurIPS-20\n\t\t\t[Strongly Incremental Constituency Parsing with Graph Neural Networks](https://proceedings.neurips.cc/paper/2020/hash/f7177163c833dff4b38fc8d2872f1ec6-Abstract.html) \n\n \t- ### Constituency\n\t\t* #### NeurIPS-20\n\t\t\t[Strongly Incremental Constituency Parsing with Graph Neural Networks](https://proceedings.neurips.cc/paper/2020/hash/f7177163c833dff4b38fc8d2872f1ec6-Abstract.html) \n\n - ## Entity Typing in KB\n\t- ### GNN for Undirected Graphs\n\t\t* #### EMNLP-19\n\t\t\t[Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks](https://www.aclweb.org/anthology/D19-1502.pdf) \n\n \t- ### Knowledge\n\t\t* #### EMNLP-19\n\t\t\t[Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks](https://www.aclweb.org/anthology/D19-1502.pdf) \n\n - ## Dependency Parsing\n\t- ### Dependency\n\t\t* #### ACL-20\n\t\t\t[Neural Reranking for Dependency Parsing: An Evaluation](https://www.aclweb.org/anthology/2020.acl-main.379/) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ACL-20\n\t\t\t[Neural Reranking for Dependency Parsing: An Evaluation](https://www.aclweb.org/anthology/2020.acl-main.379/) \n\n \t- ### App-driven\n\t\t* #### ACL-19\n\t\t\t[Graph-based Dependency Parsing with Graph Neural Networks](https://www.aclweb.org/anthology/P19-1237/) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### ACL-19\n\t\t\t[Graph-based Dependency Parsing with Graph Neural Networks](https://www.aclweb.org/anthology/P19-1237/) \n\n \t- ### Graph2Tree\n\t\t* #### ACL-19\n\t\t\t[Graph-based Dependency Parsing with Graph Neural Networks](https://www.aclweb.org/anthology/P19-1237/) \n\n - ## Text Matching\n\t- ### GNN for Directed Graphs\n\t\t* #### ACL-20\n\t\t\t[Neural Graph Matching Networks for Chinese Short Text Matching](https://www.aclweb.org/anthology/2020.acl-main.547/) \n\n \t- ### App-driven\n\t\t* #### ACL-20\n\t\t\t[Neural Graph Matching Networks for Chinese Short Text Matching](https://www.aclweb.org/anthology/2020.acl-main.547/) \n\n \t\t\t[Matching Article Pairs with Graphical Decomposition and Convolutions](https://arxiv.org/abs/1802.07459) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### ACL-20\n\t\t\t[Matching Article Pairs with Graphical Decomposition and Convolutions](https://arxiv.org/abs/1802.07459) \n\n - ## Question Generation\n\t- ### Dependency\n\t\t* #### WWW-19\n\t\t\t[Learning to Generate Questions by Learning What not to Generate](https://dl.acm.org/doi/10.1145/3308558.3313737) \n\n \t\t* #### COLING-20\n\t\t\t[Answer-driven Deep Question Generation based on Reinforcement Learning](https://www.aclweb.org/anthology/2020.coling-main.452/) \n\n \t\t* #### ICLR-20\n\t\t\t[Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://openreview.net/pdf?id=HygnDhEtvr) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ICLR-20\n\t\t\t[Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://openreview.net/pdf?id=HygnDhEtvr) \n\n \t- ### Node Embedding Based\n\t\t* #### ICLR-20\n\t\t\t[Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://openreview.net/pdf?id=HygnDhEtvr) \n\n \t- ### Graph2Seq\n\t\t* #### COLING-20\n\t\t\t[Answer-driven Deep Question Generation based on Reinforcement Learning](https://www.aclweb.org/anthology/2020.coling-main.452/) \n\n \t\t* #### ICLR-20\n\t\t\t[Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation](https://openreview.net/pdf?id=HygnDhEtvr) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### WWW-19\n\t\t\t[Learning to Generate Questions by Learning What not to Generate](https://dl.acm.org/doi/10.1145/3308558.3313737) \n\n \t\t* #### COLING-20\n\t\t\t[Answer-driven Deep Question Generation based on Reinforcement Learning](https://www.aclweb.org/anthology/2020.coling-main.452/) \n\n - ## Next Utterance Prediction\n\t- ### App-driven\n\t\t* #### AAAI-21\n\t\t\t[A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training](https://arxiv.org/abs/2012.11099) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### AAAI-21\n\t\t\t[A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training](https://arxiv.org/abs/2012.11099) \n\n - ## Code Summarization\n\t- ### GNN for Directed Graphs\n\t\t* #### ArXiv-20\n\t\t\t[Improved code summarization via a graph neural network](https://arxiv.org/pdf/2004.02843.pdf) \n\n \t\t* #### ICLR-18\n\t\t\t[learning to represent programs with graphs](https://openreview.net/pdf?id=BJOFETxR-) \n\n \t\t* #### ICLR-19\n\t\t\t[Structured Neural Summarization](https://openreview.net/pdf?id=H1ersoRqtm) \n\n \t- ### App-driven\n\t\t* #### ArXiv-20\n\t\t\t[Improved code summarization via a graph neural network](https://arxiv.org/pdf/2004.02843.pdf) \n\n \t\t* #### ICLR-18\n\t\t\t[learning to represent programs with graphs](https://openreview.net/pdf?id=BJOFETxR-) \n\n \t\t* #### ICLR-19\n\t\t\t[Structured Neural Summarization](https://openreview.net/pdf?id=H1ersoRqtm) \n\n \t- ### Graph2Seq\n\t\t* #### ArXiv-20\n\t\t\t[Improved code summarization via a graph neural network](https://arxiv.org/pdf/2004.02843.pdf) \n\n \t\t* #### ICLR-18\n\t\t\t[learning to represent programs with graphs](https://openreview.net/pdf?id=BJOFETxR-) \n\n \t\t* #### ICLR-19\n\t\t\t[Structured Neural Summarization](https://openreview.net/pdf?id=H1ersoRqtm) \n\n - ## Event Detection\n\t- ### Position\n\t\t* #### RANLP-17\n\t\t\t[Graph-based Event Extraction from Twitter](https://www.aclweb.org/anthology/R17-1031/) \n\n \t- ### Co-occurrence\n\t\t* #### RANLP-17\n\t\t\t[Graph-based Event Extraction from Twitter](https://www.aclweb.org/anthology/R17-1031/) \n\n \t- ### Dependency\n\t\t* #### AAAI-18\n\t\t\t[Graph Convolutional Networks with Argument-Aware Pooling for Event Detection](https://nyuscholars.nyu.edu/en/publications/graph-convolutional-networks-with-argument-aware-pooling-for-even) \n\n \t\t\t[Graph Convolutional Networks with Argument-Aware Pooling for Event Detection](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16329/16155) \n\n \t\t* #### EMNLP-19\n\t\t\t[Event Detection with Multi-Order Graph Convolution and Aggregated Attention](https://www.aclweb.org/anthology/D19-1582/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation](https://www.aclweb.org/anthology/2020.findings-emnlp.211/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://www.aclweb.org/anthology/D18-1156/) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ACL-19\n\t\t\t[Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media](https://www.aclweb.org/anthology/P19-1247.pdf) \n\n \t\t* #### AAAI-18\n\t\t\t[Graph Convolutional Networks with Argument-Aware Pooling for Event Detection](https://nyuscholars.nyu.edu/en/publications/graph-convolutional-networks-with-argument-aware-pooling-for-even) \n\n \t\t\t[Graph Convolutional Networks with Argument-Aware Pooling for Event Detection](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16329/16155) \n\n \t\t* #### EMNLP-19\n\t\t\t[Event Detection with Multi-Order Graph Convolution and Aggregated Attention](https://www.aclweb.org/anthology/D19-1582/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation](https://www.aclweb.org/anthology/2020.findings-emnlp.211/) \n\n \t\t\t[Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks](https://www.aclweb.org/anthology/2020.emnlp-main.583/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://www.aclweb.org/anthology/D18-1156/) \n\n \t- ### App-driven\n\t\t* #### ACL-19\n\t\t\t[Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media](https://www.aclweb.org/anthology/P19-1247.pdf) \n\n \t- ### Knowledge\n\t\t* #### EMNLP-20\n\t\t\t[Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks](https://www.aclweb.org/anthology/2020.emnlp-main.583/) \n\n - ## Natural Language Inference\n\t- ### Knowledge\n\t\t* #### AAAI-19\n\t\t\t[Improving Natural Language Inference Using External Knowledgein the Science Questions Domain](https://ojs.aaai.org//index.php/AAAI/article/view/4705) \n\n \t\t* #### AAAI-20\n\t\t\t[Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks](https://www.semanticscholar.org/paper/Infusing-Knowledge-into-the-Textual-Entailment-Task-Kapanipathi-Thost/4f8e1a4247ce06a15760fc2692c6849601d41b6f) \n\n \t\t* #### EMNLP-19\n\t\t\t[KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://www.aclweb.org/anthology/D19-1282/) \n\n - ## Semantic Role Labeling\n\t- ### Dependency\n\t\t* #### ArXiv-20\n\t\t\t[Cross-Lingual Semantic Role Labeling With Model Transfer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9165903) \n\n \t\t\t[Semantic Role Labeling with Heterogeneous Syntactic Knowledge](https://www.aclweb.org/anthology/2020.coling-main.266.pdf) \n\n \t\t* #### ACL-20\n\t\t\t[Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks](https://www.aclweb.org/anthology/2020.acl-main.297.pdf) \n\n \t\t* #### EMNLP-17\n\t\t\t[Encoding Sentences with Graph Convolutional Networks](https://www.aclweb.org/anthology/D17-1159.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[A Unified Syntax-aware Framework for Semantic Role Labeling](https://www.aclweb.org/anthology/D18-1262.pdf) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ArXiv-20\n\t\t\t[Semantic Role Labeling with Heterogeneous Syntactic Knowledge](https://www.aclweb.org/anthology/2020.coling-main.266.pdf) \n\n \t\t* #### ACL-20\n\t\t\t[Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks](https://www.aclweb.org/anthology/2020.acl-main.297.pdf) \n\n \t\t* #### EMNLP-20\n\t\t\t[Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling](https://www.aclweb.org/anthology/2020.emnlp-main.322.pdf) \n\n \t\t* #### EMNLP-17\n\t\t\t[Encoding Sentences with Graph Convolutional Networks](https://www.aclweb.org/anthology/D17-1159.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[A Unified Syntax-aware Framework for Semantic Role Labeling](https://www.aclweb.org/anthology/D18-1262.pdf) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ArXiv-20\n\t\t\t[Cross-Lingual Semantic Role Labeling With Model Transfer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9165903) \n\n \t\t\t[Semantic Role Labeling with Heterogeneous Syntactic Knowledge](https://www.aclweb.org/anthology/2020.coling-main.266.pdf) \n\n \t\t* #### EMNLP-20\n\t\t\t[Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling](https://www.aclweb.org/anthology/2020.emnlp-main.322.pdf) \n\n \t\t* #### EMNLP-17\n\t\t\t[Encoding Sentences with Graph Convolutional Networks](https://www.aclweb.org/anthology/D17-1159.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[A Unified Syntax-aware Framework for Semantic Role Labeling](https://www.aclweb.org/anthology/D18-1262.pdf) \n\n \t- ### Constituency\n\t\t* #### EMNLP-20\n\t\t\t[Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling](https://www.aclweb.org/anthology/2020.emnlp-main.322.pdf) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### ArXiv-20\n\t\t\t[Cross-Lingual Semantic Role Labeling With Model Transfer](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9165903) \n\n - ## Fact Verification\n\t- ### App-driven\n\t\t* #### ACL-19\n\t\t\t[GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://www.aclweb.org/anthology/P19-1085.pdf) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### ACL-19\n\t\t\t[GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://www.aclweb.org/anthology/P19-1085.pdf) \n\n - ## AMR Parsing\n\t- ### Graph2Graph\n\t\t* #### ACL-20\n\t\t\t[AMR Parsing with Latent Structural Information](https://www.aclweb.org/anthology/2020.acl-main.397/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Online Back-Parsing for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.92/) \n\n \t- ### Dependency\n\t\t* #### ACL-20\n\t\t\t[AMR Parsing with Latent Structural Information](https://www.aclweb.org/anthology/2020.acl-main.397/) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ACL-20\n\t\t\t[AMR Parsing with Latent Structural Information](https://www.aclweb.org/anthology/2020.acl-main.397/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Online Back-Parsing for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.92/) \n\n \t- ### App-driven\n\t\t* #### ACL-20\n\t\t\t[AMR Parsing via Graph_x001C_Sequence Iterative Inference(to be deleted)](https://www.aclweb.org/anthology/2020.acl-main.119/) \n\n \t\t* #### ACL-19\n\t\t\t[AMR Parsing as Sequence-to-Graph Transduction(to be deleted)](https://www.aclweb.org/anthology/P19-1009/) \n\n \t\t* #### ACL-18\n\t\t\t[AMR Parsing as Graph Prediction with Latent Alignment(to be deleted)](https://www.aclweb.org/anthology/P18-1037/) \n\n \t- ### AMR\n\t\t* #### EMNLP-20\n\t\t\t[Online Back-Parsing for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.92/) \n\n \t- ### Graph2Seq\n\t\t* #### EMNLP-20\n\t\t\t[Online Back-Parsing for AMR-to-Text Generation](https://www.aclweb.org/anthology/2020.emnlp-main.92/) \n\n - ## Sentiment Analysis\n\t- ### Dependency\n\t\t* #### ACL-20\n\t\t\t[Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification](https://www.aclweb.org/anthology/2020.acl-main.588/) \n\n \t\t\t[Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://www.aclweb.org/anthology/2020.acl-main.295) \n\n \t\t* #### EMNLP-19\n\t\t\t[Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks](https://www.aclweb.org/anthology/D19-1549) \n\n \t\t\t[Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://www.aclweb.org/anthology/D19-1464) \n\n \t\t* #### EMNLP-20\n\t\t\t[Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation](https://www.aclweb.org/anthology/2020.findings-emnlp.407) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ACL-20\n\t\t\t[KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis](https://www.aclweb.org/anthology/2020.acl-main.292/) \n\n \t\t\t[Aspect Sentiment Classification with Document-level Sentiment Preference Modeling](https://www.aclweb.org/anthology/2020.acl-main.338/) \n\n \t\t\t[Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification](https://www.aclweb.org/anthology/2020.acl-main.588/) \n\n \t\t\t[Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://www.aclweb.org/anthology/2020.acl-main.295) \n\n \t\t* #### EMNLP-19\n\t\t\t[Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks](https://www.aclweb.org/anthology/D19-1549) \n\n \t- ### GNN for undirected graphs\n\t\t* #### EMNLP-20\n\t\t\t[Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis](https://www.aclweb.org/anthology/2020.emnlp-main.286) \n\n \t- ### Similarity\n\t\t* #### ACL-20\n\t\t\t[Aspect Sentiment Classification with Document-level Sentiment Preference Modeling](https://www.aclweb.org/anthology/2020.acl-main.338/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis](https://www.aclweb.org/anthology/2020.emnlp-main.286/) \n\n \t\t\t[Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis](https://www.aclweb.org/anthology/2020.emnlp-main.286) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### EMNLP-19\n\t\t\t[Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://www.aclweb.org/anthology/D19-1464) \n\n \t\t* #### EMNLP-20\n\t\t\t[Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation](https://www.aclweb.org/anthology/2020.findings-emnlp.407) \n\n \t- ### GNN on Undirected Graphs\n\t\t* #### EMNLP-20\n\t\t\t[Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis](https://www.aclweb.org/anthology/2020.emnlp-main.286/) \n\n \t- ### Knowledge\n\t\t* #### ACL-20\n\t\t\t[KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis](https://www.aclweb.org/anthology/2020.acl-main.292/) \n\n - ## Semantic Parsing\n\t- ### Dependency\n\t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Exploiting Rich Syntactic Information for Semantic Parsingwith Graph-to-Sequence Mode](https://www.aclweb.org/anthology/D18-1110/) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ACL-19\n\t\t\t[Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing](https://www.aclweb.org/anthology/P19-1448/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n \t- ### App-driven\n\t\t* #### AAAI-20\n\t\t\t[Graph-Based Transformer with Cross-Candidate Verification for Semantic Parsing](https://ojs.aaai.org//index.php/AAAI/article/view/6408) \n\n \t\t* #### ACL-19\n\t\t\t[Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing](https://www.aclweb.org/anthology/P19-1448/) \n\n \t\t* #### EMNLP-19\n\t\t\t[Global Reasoning over Database Structures for Text-to-SQL Parsing](https://www.aclweb.org/anthology/D19-1378/) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Exploiting Rich Syntactic Information for Semantic Parsingwith Graph-to-Sequence Mode](https://www.aclweb.org/anthology/D18-1110/) \n\n \t- ### Constituency\n\t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Exploiting Rich Syntactic Information for Semantic Parsingwith Graph-to-Sequence Mode](https://www.aclweb.org/anthology/D18-1110/) \n\n \t- ### Graph2Seq\n\t\t* #### EMNLP-18\n\t\t\t[Exploiting Rich Syntactic Information for Semantic Parsingwith Graph-to-Sequence Mode](https://www.aclweb.org/anthology/D18-1110/) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### EMNLP-18\n\t\t\t[Exploiting Rich Syntactic Information for Semantic Parsingwith Graph-to-Sequence Mode](https://www.aclweb.org/anthology/D18-1110/) \n\n \t- ### Graph2Tree\n\t\t* #### EMNLP-20\n\t\t\t[Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem](https://www.aclweb.org/anthology/2020.findings-emnlp.255/) \n\n - ## Knowledge Graph Embedding\n\t- ### GNN for Directed Graphs\n\t\t* #### ACL-19\n\t\t\t[Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://www.aclweb.org/anthology/P19-1466.pdf) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ACL-20\n\t\t\t[ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding](https://www.aclweb.org/anthology/2020.acl-main.526.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[CaRe: Open Knowledge Graph Embeddings](https://www.aclweb.org/anthology/D19-1036.pdf) \n\n \t\t* #### IJCAI-19\n\t\t\t[A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment](https://www.ijcai.org/Proceedings/2019/0574.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://www.aclweb.org/anthology/N19-1306.pdf) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### ACL-19\n\t\t\t[A2N: Attending to Neighbors for Knowledge Graph Inference](https://www.aclweb.org/anthology/P19-1431.pdf) \n\n \t- ### Knowledge\n\t\t* #### AAAI-19\n\t\t\t[Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding](https://ojs.aaai.org//index.php/AAAI/article/view/4698) \n\n \t\t* #### ACL-20\n\t\t\t[ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding](https://www.aclweb.org/anthology/2020.acl-main.526.pdf) \n\n \t\t* #### ACL-19\n\t\t\t[Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://www.aclweb.org/anthology/P19-1466.pdf) \n\n \t\t\t[A2N: Attending to Neighbors for Knowledge Graph Inference](https://www.aclweb.org/anthology/P19-1431.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[CaRe: Open Knowledge Graph Embeddings](https://www.aclweb.org/anthology/D19-1036.pdf) \n\n \t\t* #### IJCAI-19\n\t\t\t[A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment](https://www.ijcai.org/Proceedings/2019/0574.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://www.aclweb.org/anthology/N19-1306.pdf) \n\n - ## Knowledge Base Completion\n\t- ### GNN for Directed Graphs\n\t\t* #### ACL-19\n\t\t\t[Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://www.aclweb.org/anthology/P19-1466.pdf) \n\n \t\t\t[Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://www.aclweb.org/anthology/P19-1466.pdf) \n\n \t\t* #### ICLR-20\n\t\t\t[DYNAMICALLY PRUNED MESSAGE PASSING NETWORKS FOR LARGE-SCALE KNOWLEDGE GRAPH\nREASONING](https://openreview.net/pdf?id=rkeuAhVKvB) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### AAAI-20\n\t\t\t[Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion](https://ojs.aaai.org//index.php/AAAI/article/view/6508) \n\n \t\t* #### ESWC 2018\n\t\t\t[Modeling Relational Data with Graph Convolutional Networks](https://link.springer.com/chapter/10.1007/978-3-319-93417-4_38) \n\n \t\t* #### ACL-19\n\t\t\t[Multi-Channel Graph Neural Network for Entity Alignment](https://www.aclweb.org/anthology/P19-1140.pdf) \n\n \t\t* #### ICML-20\n\t\t\t[Inductive Relation Prediction by Subgraph Reasoning](http://proceedings.mlr.press/v119/teru20a/teru20a.pdf) \n\n \t\t* #### EMNLP-20\n\t\t\t[TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion](https://www.aclweb.org/anthology/2020.emnlp-main.462.pdf) \n\n \t\t* #### IJCAI-19\n\t\t\t[Robust Embedding with Multi-Level Structures for Link Prediction](https://www.ijcai.org/Proceedings/2019/0728.pdf) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### AAAI-19\n\t\t\t[End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion](https://ojs.aaai.org//index.php/AAAI/article/view/4164) \n\n \t\t* #### AAAI-20\n\t\t\t[Commonsense Knowledge Base Completion with Structural and Semantic Context](https://ojs.aaai.org/index.php/AAAI/article/download/5684/5540) \n\n \t\t* #### ACL-19\n\t\t\t[A2N: Attending to Neighbors for Knowledge Graph Inference](https://www.aclweb.org/anthology/P19-1431.pdf) \n\n \t\t* #### ICML-20\n\t\t\t[Inductive Relation Prediction by Subgraph Reasoning](http://proceedings.mlr.press/v119/teru20a/teru20a.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning](https://www.aclweb.org/anthology/D19-1264.pdf) \n\n \t- ### Knowledge\n\t\t* #### AAAI-19\n\t\t\t[End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion](https://ojs.aaai.org//index.php/AAAI/article/view/4164) \n\n \t\t* #### AAAI-20\n\t\t\t[Commonsense Knowledge Base Completion with Structural and Semantic Context](https://ojs.aaai.org/index.php/AAAI/article/download/5684/5540) \n\n \t\t\t[Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion](https://ojs.aaai.org//index.php/AAAI/article/view/6508) \n\n \t\t* #### ESWC 2018\n\t\t\t[Modeling Relational Data with Graph Convolutional Networks](https://link.springer.com/chapter/10.1007/978-3-319-93417-4_38) \n\n \t\t* #### ACL-19\n\t\t\t[Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://www.aclweb.org/anthology/P19-1466.pdf) \n\n \t\t\t[A2N: Attending to Neighbors for Knowledge Graph Inference](https://www.aclweb.org/anthology/P19-1431.pdf) \n\n \t\t\t[Multi-Channel Graph Neural Network for Entity Alignment](https://www.aclweb.org/anthology/P19-1140.pdf) \n\n \t\t\t[Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://www.aclweb.org/anthology/P19-1466.pdf) \n\n \t\t* #### ICML-20\n\t\t\t[Inductive Relation Prediction by Subgraph Reasoning](http://proceedings.mlr.press/v119/teru20a/teru20a.pdf) \n\n \t\t\t[Inductive Relation Prediction by Subgraph Reasoning](http://proceedings.mlr.press/v119/teru20a/teru20a.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning](https://www.aclweb.org/anthology/D19-1264.pdf) \n\n \t\t* #### EMNLP-20\n\t\t\t[TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion](https://www.aclweb.org/anthology/2020.emnlp-main.462.pdf) \n\n \t\t* #### IJCAI-19\n\t\t\t[Robust Embedding with Multi-Level Structures for Link Prediction](https://www.ijcai.org/Proceedings/2019/0728.pdf) \n\n \t\t* #### ICLR-20\n\t\t\t[DYNAMICALLY PRUNED MESSAGE PASSING NETWORKS FOR LARGE-SCALE KNOWLEDGE GRAPH\nREASONING](https://openreview.net/pdf?id=rkeuAhVKvB) \n\n - ## Summarization\n\t- ### Co-occurrence\n\t\t* #### EMNLP-20\n\t\t\t[Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network](https://www.aclweb.org/anthology/2020.emnlp-main.295/) \n\n \t- ### Coreference\n\t\t* #### ACL-20\n\t\t\t[Discourse-Aware Neural Extractive Text Summarization](https://www.aclweb.org/anthology/2020.acl-main.451.pdf) \n\n \t- ### Dependency\n\t\t* #### AAAI-20\n\t\t\t[SemSUM: Semantic Dependency Guided Neural Abstractive Summarization](https://ojs.aaai.org//index.php/AAAI/article/view/6312) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### AAAI-20\n\t\t\t[SemSUM: Semantic Dependency Guided Neural Abstractive Summarization](https://ojs.aaai.org//index.php/AAAI/article/view/6312) \n\n \t\t* #### COLING-20\n\t\t\t[Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words](https://www.aclweb.org/anthology/2020.coling-main.39/) \n\n \t\t* #### ACL-20\n\t\t\t[Discourse-Aware Neural Extractive Text Summarization](https://www.aclweb.org/anthology/2020.acl-main.451.pdf) \n\n \t\t\t[Discourse-Aware Neural Extractive Text Summarization](https://www.aclweb.org/anthology/2020.acl-main.451.pdf) \n\n \t\t\t[Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://www.aclweb.org/anthology/2020.acl-main.457.pdf) \n\n \t\t\t[Leveraging Graph to Improve Abstractive Multi-Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.555.pdf) \n\n \t\t* #### EMNLP-20\n\t\t\t[Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network](https://www.aclweb.org/anthology/2020.emnlp-main.295/) \n\n \t\t\t[Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention](https://www.aclweb.org/anthology/2020.findings-emnlp.2/) \n\n \t\t* #### ACL-17\n\t\t\t[Abstractive document summarization with a graph-based attentional neural model](https://www.aclweb.org/anthology/P17-1108/) \n\n \t- ### AMR\n\t\t* #### COLING-18\n\t\t\t[Abstract Meaning Representation for Multi-Document Summarization](https://www.aclweb.org/anthology/C18-1101.pdf) \n\n \t- ### Discourse\n\t\t* #### ACL-20\n\t\t\t[Discourse-Aware Neural Extractive Text Summarization](https://www.aclweb.org/anthology/2020.acl-main.451.pdf) \n\n \t\t\t[Discourse-Aware Neural Extractive Text Summarization](https://www.aclweb.org/anthology/2020.acl-main.451.pdf) \n\n \t\t\t[Leveraging Graph to Improve Abstractive Multi-Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.555.pdf) \n\n \t\t* #### COLING-17\n\t\t\t[Graph-based Neural Multi-Document Summarization](https://www.aclweb.org/anthology/K17-1045/) \n\n \t\t* #### ACL-17\n\t\t\t[Abstractive document summarization with a graph-based attentional neural model](https://www.aclweb.org/anthology/P17-1108/) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ACL-20\n\t\t\t[Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.553.pdf) \n\n \t- ### Node Embedding Based\n\t\t* #### COLING-20\n\t\t\t[Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks](https://www.aclweb.org/anthology/2020.coling-main.468.pdf) \n\n \t- ### Similarity\n\t\t* #### COLING-20\n\t\t\t[Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words](https://www.aclweb.org/anthology/2020.coling-main.39/) \n\n \t\t* #### ACL-20\n\t\t\t[Leveraging Graph to Improve Abstractive Multi-Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.555.pdf) \n\n \t\t\t[Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.553.pdf) \n\n \t\t* #### EMNLP-20\n\t\t\t[Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network](https://www.aclweb.org/anthology/2020.emnlp-main.295/) \n\n \t\t\t[Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention](https://www.aclweb.org/anthology/2020.findings-emnlp.2/) \n\n \t\t* #### COLING-17\n\t\t\t[Graph-based Neural Multi-Document Summarization](https://www.aclweb.org/anthology/K17-1045/) \n\n \t- ### Graph2Seq\n\t\t* #### COLING-20\n\t\t\t[Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words](https://www.aclweb.org/anthology/2020.coling-main.39/) \n\n \t\t\t[Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks](https://www.aclweb.org/anthology/2020.coling-main.468.pdf) \n\n \t\t* #### ACL-20\n\t\t\t[Discourse-Aware Neural Extractive Text Summarization](https://www.aclweb.org/anthology/2020.acl-main.451.pdf) \n\n \t\t\t[Discourse-Aware Neural Extractive Text Summarization](https://www.aclweb.org/anthology/2020.acl-main.451.pdf) \n\n \t\t\t[Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://www.aclweb.org/anthology/2020.acl-main.457.pdf) \n\n \t\t\t[Leveraging Graph to Improve Abstractive Multi-Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.555.pdf) \n\n \t\t\t[Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.553.pdf) \n\n \t\t* #### EMNLP-20\n\t\t\t[Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network](https://www.aclweb.org/anthology/2020.emnlp-main.295/) \n\n \t\t\t[Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention](https://www.aclweb.org/anthology/2020.findings-emnlp.2/) \n\n \t\t* #### COLING-17\n\t\t\t[Graph-based Neural Multi-Document Summarization](https://www.aclweb.org/anthology/K17-1045/) \n\n \t\t* #### COLING-18\n\t\t\t[Abstract Meaning Representation for Multi-Document Summarization](https://www.aclweb.org/anthology/C18-1101.pdf) \n\n \t\t* #### ACL-17\n\t\t\t[Abstractive document summarization with a graph-based attentional neural model](https://www.aclweb.org/anthology/P17-1108/) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### COLING-17\n\t\t\t[Graph-based Neural Multi-Document Summarization](https://www.aclweb.org/anthology/K17-1045/) \n\n \t- ### IE\n\t\t* #### ACL-20\n\t\t\t[Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward](https://www.aclweb.org/anthology/2020.acl-main.457.pdf) \n\n \t- ### Topic\n\t\t* #### ACL-20\n\t\t\t[Leveraging Graph to Improve Abstractive Multi-Document Summarization](https://www.aclweb.org/anthology/2020.acl-main.555.pdf) \n\n - ## Text Classification\n\t- ### Coreference\n\t\t* #### ACL-20\n\t\t\t[Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://www.aclweb.org/anthology/2020.acl-main.31.pdf) \n\n \t- ### App-driven\n\t\t* #### AAAI-19\n\t\t\t[Graph Convolutional Networks for Text Classification](https://ojs.aaai.org//index.php/AAAI/article/view/4725) \n\n \t\t* #### AAAI-20\n\t\t\t[Tensor Graph Convolutional Networks for Text Classification](https://ojs.aaai.org//index.php/AAAI/article/view/6359) \n\n \t\t* #### EMNLP-19\n\t\t\t[Text Level Graph Neural Network for Text Classification](https://www.aclweb.org/anthology/D19-1345/) \n\n \t\t\t[Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification](https://www.aclweb.org/anthology/D19-1488/) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### AAAI-20\n\t\t\t[Tensor Graph Convolutional Networks for Text Classification](https://ojs.aaai.org//index.php/AAAI/article/view/6359) \n\n \t\t* #### EMNLP-19\n\t\t\t[Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification](https://www.aclweb.org/anthology/D19-1488/) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### AAAI-19\n\t\t\t[Graph Convolutional Networks for Text Classification](https://ojs.aaai.org//index.php/AAAI/article/view/4725) \n\n \t\t* #### ACL-20\n\t\t\t[Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://www.aclweb.org/anthology/2020.acl-main.31.pdf) \n\n \t\t* #### EMNLP-19\n\t\t\t[Text Level Graph Neural Network for Text Classification](https://www.aclweb.org/anthology/D19-1345/) \n\n - ## Relation Extraction\n\t- ### Node Embedding Based Refined\n\t\t* #### NAACL-19\n\t\t\t[A General Framework for Information Extraction using Dynamic Span Graphs](https://www.aclweb.org/anthology/N19-1308.pdf) \n\n \t- ### Co-occurrence\n\t\t* #### EMNLP-19\n\t\t\t[Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://www.aclweb.org/anthology/D19-1498/) \n\n \t- ### Document\n\t\t* #### EMNLP-19\n\t\t\t[Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://www.aclweb.org/anthology/D19-1498/) \n\n \t- ### Coreference\n\t\t* #### ACL-19\n\t\t\t[Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://www.aclweb.org/anthology/P19-1423/) \n\n \t\t* #### NAACL-19\n\t\t\t[A General Framework for Information Extraction using Dynamic Span Graphs](https://www.aclweb.org/anthology/N19-1308.pdf) \n\n \t- ### Dependency\n\t\t* #### ACL-19\n\t\t\t[GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction](https://www.aclweb.org/anthology/P19-1136.pdf) \n\n \t\t\t[Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://www.aclweb.org/anthology/P19-1423/) \n\n \t\t\t[Attention Guided Graph Convolutional Networks for Relation Extraction](https://www.aclweb.org/anthology/P19-1024/) \n\n \t\t\t[Attention Guided Graph Convolutional Networks for Relation Extraction](https://www.aclweb.org/anthology/P19-1024.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[GraphIE: A Graph-Based Framework for Information Extraction](https://www.aclweb.org/anthology/N19-1082/) \n\n \t\t* #### EMNLP-18\n\t\t\t[Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://www.aclweb.org/anthology/D18-1244.pdf) \n\n \t\t\t[RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://www.aclweb.org/anthology/D18-1157/) \n\n \t\t\t[N-ary Relation Extraction using Graph State LSTM](https://www.aclweb.org/anthology/D18-1246.pdf) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ACL-20\n\t\t\t[Structural Information Preserving for Graph-to-Text Generation](https://www.aclweb.org/anthology/2020.acl-main.712/) \n\n \t\t* #### ACL-19\n\t\t\t[Attention Guided Graph Convolutional Networks for Relation Extraction](https://www.aclweb.org/anthology/P19-1024.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://www.aclweb.org/anthology/D18-1244.pdf) \n\n \t\t\t[RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://www.aclweb.org/anthology/D18-1157/) \n\n \t\t\t[N-ary Relation Extraction using Graph State LSTM](https://www.aclweb.org/anthology/D18-1246.pdf) \n\n \t- ### App-driven\n\t\t* #### ACL-20\n\t\t\t[Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction](https://www.aclweb.org/anthology/2020.acl-main.342.pdf) \n\n \t\t* #### ICML-20\n\t\t\t[Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs](http://proceedings.mlr.press/v119/qu20a/qu20a.pdf) \n\n \t\t* #### EMNLP-20\n\t\t\t[Double Graph Based Reasoning for Document-level Relation Extraction](https://www.aclweb.org/anthology/2020.emnlp-main.127.pdf) \n\n \t- ### AMR\n\t\t* #### ACL-20\n\t\t\t[Structural Information Preserving for Graph-to-Text Generation](https://www.aclweb.org/anthology/2020.acl-main.712/) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ACL-19\n\t\t\t[GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction](https://www.aclweb.org/anthology/P19-1136.pdf) \n\n \t\t\t[Joint Type Inference on Entities and Relations via Graph Convolutional Networks](https://www.aclweb.org/anthology/P19-1131/) \n\n \t\t\t[Graph Neural Networks with Generated Parameters for Relation Extraction](https://www.aclweb.org/anthology/P19-1128/) \n\n \t\t* #### EMNLP-19\n\t\t\t[Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://www.aclweb.org/anthology/D19-1498/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Double Graph Based Reasoning for Document-level Relation Extraction](https://www.aclweb.org/anthology/2020.emnlp-main.127.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://www.aclweb.org/anthology/N19-1306.pdf) \n\n \t- ### Graph2Seq\n\t\t* #### ACL-20\n\t\t\t[Structural Information Preserving for Graph-to-Text Generation](https://www.aclweb.org/anthology/2020.acl-main.712/) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### ACL-19\n\t\t\t[Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://www.aclweb.org/anthology/P19-1423/) \n\n \t\t\t[Attention Guided Graph Convolutional Networks for Relation Extraction](https://www.aclweb.org/anthology/P19-1024/) \n\n \t\t* #### ICML-20\n\t\t\t[Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs](http://proceedings.mlr.press/v119/qu20a/qu20a.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[GraphIE: A Graph-Based Framework for Information Extraction](https://www.aclweb.org/anthology/N19-1082/) \n\n \t- ### IE\n\t\t* #### EMNLP-18\n\t\t\t[RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://www.aclweb.org/anthology/D18-1157/) \n\n \t- ### Knowledge\n\t\t* #### ACL-20\n\t\t\t[Structural Information Preserving for Graph-to-Text Generation](https://www.aclweb.org/anthology/2020.acl-main.712/) \n\n \t\t* #### NAACL-19\n\t\t\t[Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://www.aclweb.org/anthology/N19-1306.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://www.aclweb.org/anthology/D18-1157/) \n\n - ## Machine Reading Comprehension\n\t- ### Co-occurrence\n\t\t* #### ACL-19\n\t\t\t[Dynamically Fused Graph Network for Multi-hop Reasoning](https://www.aclweb.org/anthology/P19-1617/) \n\n \t- ### Coreference\n\t\t* #### NAACL-19\n\t\t\t[Question Answering by Reasoning Across Documents with Graph Convolutional Networks](https://www.aclweb.org/anthology/N19-1240/) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### ACL-20\n\t\t\t[Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings](https://www.aclweb.org/anthology/2020.acl-main.412/) \n\n \t\t\t[Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension](https://www.aclweb.org/anthology/2020.acl-main.599/) \n\n \t\t* #### ACL-19\n\t\t\t[Dynamically Fused Graph Network for Multi-hop Reasoning](https://www.aclweb.org/anthology/P19-1617/) \n\n \t- ### App-driven\n\t\t* #### ACL-20\n\t\t\t[Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks](https://www.aclweb.org/anthology/D19-5306/) \n\n \t\t\t[Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension](https://www.aclweb.org/anthology/2020.acl-main.599/) \n\n \t\t* #### ACL-19\n\t\t\t[Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs](https://www.aclweb.org/anthology/P19-1260/) \n\n \t\t\t[Cognitive Graph for Multi-Hop Reading Comprehension at Scale](https://www.aclweb.org/anthology/P19-1259/) \n\n \t\t* #### EMNLP-19\n\t\t\t[NumNet: Machine Reading Comprehension with Numerical Reasoning](https://www.aclweb.org/anthology/D19-1251/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Hierarchical Graph Network for Multi-hop Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.710/) \n\n \t\t\t[SRLGRN: Semantic Role Labeling Graph Reasoning Network](https://www.aclweb.org/anthology/2020.emnlp-main.714.pdf) \n\n \t\t* #### IJCAI-20\n\t\t\t[Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network](https://www.ijcai.org/Proceedings/2020/540) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ACL-19\n\t\t\t[Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs](https://www.aclweb.org/anthology/P19-1260/) \n\n \t\t* #### EMNLP-19\n\t\t\t[NumNet: Machine Reading Comprehension with Numerical Reasoning](https://www.aclweb.org/anthology/D19-1251/) \n\n \t\t* #### EMNLP-20\n\t\t\t[SRLGRN: Semantic Role Labeling Graph Reasoning Network](https://www.aclweb.org/anthology/2020.emnlp-main.714.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[Question Answering by Reasoning Across Documents with Graph Convolutional Networks](https://www.aclweb.org/anthology/N19-1240/) \n\n \t\t* #### IJCAI-20\n\t\t\t[Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network](https://www.ijcai.org/Proceedings/2020/540) \n\n \t- ### Node Embedding Based\n\t\t* #### IJCAI-20\n\t\t\t[GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension](https://www.ijcai.org/Proceedings/2020/171) \n\n \t- ### Graph2Seq\n\t\t* #### ACL-20\n\t\t\t[Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings](https://www.aclweb.org/anthology/2020.acl-main.412/) \n\n \t\t\t[Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks](https://www.aclweb.org/anthology/D19-5306/) \n\n \t\t* #### ACL-19\n\t\t\t[Dynamically Fused Graph Network for Multi-hop Reasoning](https://www.aclweb.org/anthology/P19-1617/) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### ACL-20\n\t\t\t[Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks](https://www.aclweb.org/anthology/D19-5306/) \n\n \t\t* #### ACL-19\n\t\t\t[Cognitive Graph for Multi-Hop Reading Comprehension at Scale](https://www.aclweb.org/anthology/P19-1259/) \n\n \t\t* #### EMNLP-20\n\t\t\t[Hierarchical Graph Network for Multi-hop Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.710/) \n\n \t\t\t[SRLGRN: Semantic Role Labeling Graph Reasoning Network](https://www.aclweb.org/anthology/2020.emnlp-main.714.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering](https://www.aclweb.org/anthology/N19-1032/) \n\n \t\t* #### IJCAI-20\n\t\t\t[GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension](https://www.ijcai.org/Proceedings/2020/171) \n\n \t- ### IE\n\t\t* #### EMNLP-20\n\t\t\t[SRLGRN: Semantic Role Labeling Graph Reasoning Network](https://www.aclweb.org/anthology/2020.emnlp-main.714.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering](https://www.aclweb.org/anthology/N19-1032/) \n\n \t- ### Knowledge\n\t\t* #### ACL-20\n\t\t\t[Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings](https://www.aclweb.org/anthology/2020.acl-main.412/) \n\n - ## Community Question Answering\n\t- ### Node Embedding Based\n\t\t* #### MM-20\n\t\t\t[Multi-modal Attentive Graph Pooling Model for Community Question Answer Matching](https://dl.acm.org/doi/pdf/10.1145/3394171.3413711) \n\n \t\t* #### MM-19\n\t\t\t[Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching](https://dl.acm.org/doi/10.1145/3343031.3350966) \n\n \t- ### Similarity\n\t\t* #### MM-20\n\t\t\t[Multi-modal Attentive Graph Pooling Model for Community Question Answer Matching](https://dl.acm.org/doi/pdf/10.1145/3394171.3413711) \n\n \t- ### Graph2Seq\n\t\t* #### MM-20\n\t\t\t[Multi-modal Attentive Graph Pooling Model for Community Question Answer Matching](https://dl.acm.org/doi/pdf/10.1145/3394171.3413711) \n\n \t\t* #### MM-19\n\t\t\t[Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching](https://dl.acm.org/doi/10.1145/3343031.3350966) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### MM-20\n\t\t\t[Multi-modal Attentive Graph Pooling Model for Community Question Answer Matching](https://dl.acm.org/doi/pdf/10.1145/3394171.3413711) \n\n \t\t* #### MM-19\n\t\t\t[Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching](https://dl.acm.org/doi/10.1145/3343031.3350966) \n\n - ## SQL2Text\n\t- ### GNN for Directed Graphs\n\t\t* #### ArXiv-18\n\t\t\t[Graph2seq: Graph to sequence learning with attention-based neural networks](https://arxiv.org/pdf/1804.00823.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[SQL-to-Text Generation with Graph-to-Sequence Model](https://www.aclweb.org/anthology/D18-1112/) \n\n \t- ### App-driven\n\t\t* #### EMNLP-19\n\t\t\t[Graph Enhanced Cross-Domain Text-to-SQL Generation](https://www.aclweb.org/anthology/D19-5319/) \n\n \t\t* #### ArXiv-18\n\t\t\t[Graph2seq: Graph to sequence learning with attention-based neural networks](https://arxiv.org/pdf/1804.00823.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[SQL-to-Text Generation with Graph-to-Sequence Model](https://www.aclweb.org/anthology/D18-1112/) \n\n \t- ### Graph2Seq\n\t\t* #### EMNLP-19\n\t\t\t[Graph Enhanced Cross-Domain Text-to-SQL Generation](https://www.aclweb.org/anthology/D19-5319/) \n\n \t\t* #### ArXiv-18\n\t\t\t[Graph2seq: Graph to sequence learning with attention-based neural networks](https://arxiv.org/pdf/1804.00823.pdf) \n\n \t\t* #### EMNLP-18\n\t\t\t[SQL-to-Text Generation with Graph-to-Sequence Model](https://www.aclweb.org/anthology/D18-1112/) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### EMNLP-19\n\t\t\t[Graph Enhanced Cross-Domain Text-to-SQL Generation](https://www.aclweb.org/anthology/D19-5319/) \n\n - ## Machine Translation\n\t- ### Coreference\n\t\t* #### AAAI-21\n\t\t\t[Document Graph for Neural Machine Translation](https://arxiv.org/pdf/2012.03477.pdf) \n\n \t\t* #### NAACL-19\n\t\t\t[Text Generation from Knowledge Graphs with Graph Transformers](https://www.aclweb.org/anthology/N19-1238/) \n\n \t- ### Dependency\n\t\t* #### AAAI-21\n\t\t\t[Document Graph for Neural Machine Translation](https://arxiv.org/pdf/2012.03477.pdf) \n\n \t\t* #### EMNLP-17\n\t\t\t[Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://www.aclweb.org/anthology/D17-1209v2.pdf) \n\n \t\t* #### ACL-18\n\t\t\t[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://www.aclweb.org/anthology/P18-1026/) \n\n \t\t* #### NAACL-18\n\t\t\t[Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks](https://www.aclweb.org/anthology/N18-2078/) \n\n \t\t* #### EACL-17\n\t\t\t[Context-Aware Graph Segmentation for Graph-Based Translation](https://www.aclweb.org/anthology/E17-2095.pdf) \n\n \t- ### GNN for Directed Graphs\n\t\t* #### AAAI-20\n\t\t\t[Graph Transformer for Graph-to-Sequence Learning](https://ojs.aaai.org//index.php/AAAI/article/view/6243) \n\n \t\t* #### ACL-19\n\t\t\t[Lattice-Based Transformer Encoder for Neural Machine Translation](https://www.aclweb.org/anthology/P19-1298/) \n\n \t\t* #### TACL-20\n\t\t\t[AMR-To-Text Generation with Graph Transformer](https://www.aclweb.org/anthology/2020.tacl-1.2.pdf) \n\n \t\t* #### AAAI-18\n\t\t\t[Graph Based Translation Memory for Neural Machine Translation](https://ojs.aaai.org/index.php/AAAI/article/view/4716) \n\n \t\t* #### AAAI-21\n\t\t\t[Document Graph for Neural Machine Translation](https://arxiv.org/pdf/2012.03477.pdf) \n\n \t\t* #### EMNLP-17\n\t\t\t[Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://www.aclweb.org/anthology/D17-1209v2.pdf) \n\n \t\t\t[Neural Machine Translation with Source-Side Latent Graph Parsing](https://www.aclweb.org/anthology/D17-1012/) \n\n \t\t* #### ACL-18\n\t\t\t[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://www.aclweb.org/anthology/P18-1026/) \n\n \t\t* #### NAACL-19\n\t\t\t[Text Generation from Knowledge Graphs with Graph Transformers](https://www.aclweb.org/anthology/N19-1238/) \n\n \t\t* #### IJCAI-20\n\t\t\t[Knowledge Graphs Enhanced Neural Machine Translation](https://www.ijcai.org/Proceedings/2020/559) \n\n \t\t* #### TACL-19\n\t\t\t[Semantic neural machine translation using AMR](https://www.aclweb.org/anthology/Q19-1002.pdf) \n\n \t\t* #### NAACL-18\n\t\t\t[Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks](https://www.aclweb.org/anthology/N18-2078/) \n\n \t- ### App-driven\n\t\t* #### ACL-20\n\t\t\t[A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://www.aclweb.org/anthology/2020.acl-main.273/) \n\n \t\t* #### ACL-19\n\t\t\t[Lattice-Based Transformer Encoder for Neural Machine Translation](https://www.aclweb.org/anthology/P19-1298/) \n\n \t\t* #### AAAI-18\n\t\t\t[Graph Based Translation Memory for Neural Machine Translation](https://ojs.aaai.org/index.php/AAAI/article/view/4716) \n\n \t- ### AMR\n\t\t* #### AAAI-20\n\t\t\t[Graph Transformer for Graph-to-Sequence Learning](https://ojs.aaai.org//index.php/AAAI/article/view/6243) \n\n \t\t* #### ACL-20\n\t\t\t[Heterogeneous Graph Transformer for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/2020.acl-main.640.pdf) \n\n \t\t* #### TACL-20\n\t\t\t[AMR-To-Text Generation with Graph Transformer](https://www.aclweb.org/anthology/2020.tacl-1.2.pdf) \n\n \t\t* #### ACL-18\n\t\t\t[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://www.aclweb.org/anthology/P18-1026/) \n\n \t\t* #### TACL-19\n\t\t\t[Semantic neural machine translation using AMR](https://www.aclweb.org/anthology/Q19-1002.pdf) \n\n \t\t\t[Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/Q19-1019/) \n\n \t- ### GNN for Heterogeneous Graphs\n\t\t* #### ACL-20\n\t\t\t[Heterogeneous Graph Transformer for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/2020.acl-main.640.pdf) \n\n \t- ### Node Embedding Based\n\t\t* #### EMNLP-17\n\t\t\t[Neural Machine Translation with Source-Side Latent Graph Parsing](https://www.aclweb.org/anthology/D17-1012/) \n\n \t- ### Graph2Seq\n\t\t* #### AAAI-20\n\t\t\t[Graph Transformer for Graph-to-Sequence Learning](https://ojs.aaai.org//index.php/AAAI/article/view/6243) \n\n \t\t* #### COLING-20\n\t\t\t[Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity](https://www.aclweb.org/anthology/2020.coling-main.397/) \n\n \t\t* #### ACL-20\n\t\t\t[Heterogeneous Graph Transformer for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/2020.acl-main.640.pdf) \n\n \t\t\t[A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://www.aclweb.org/anthology/2020.acl-main.273/) \n\n \t\t* #### ACL-19\n\t\t\t[Lattice-Based Transformer Encoder for Neural Machine Translation](https://www.aclweb.org/anthology/P19-1298/) \n\n \t\t* #### TACL-20\n\t\t\t[AMR-To-Text Generation with Graph Transformer](https://www.aclweb.org/anthology/2020.tacl-1.2.pdf) \n\n \t\t* #### AAAI-18\n\t\t\t[Graph Based Translation Memory for Neural Machine Translation](https://ojs.aaai.org/index.php/AAAI/article/view/4716) \n\n \t\t* #### AAAI-21\n\t\t\t[Document Graph for Neural Machine Translation](https://arxiv.org/pdf/2012.03477.pdf) \n\n \t\t* #### EMNLP-17\n\t\t\t[Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://www.aclweb.org/anthology/D17-1209v2.pdf) \n\n \t\t\t[Neural Machine Translation with Source-Side Latent Graph Parsing](https://www.aclweb.org/anthology/D17-1012/) \n\n \t\t* #### ACL-18\n\t\t\t[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://www.aclweb.org/anthology/P18-1026/) \n\n \t\t* #### NAACL-19\n\t\t\t[Text Generation from Knowledge Graphs with Graph Transformers](https://www.aclweb.org/anthology/N19-1238/) \n\n \t\t* #### IJCAI-20\n\t\t\t[Knowledge Graphs Enhanced Neural Machine Translation](https://www.ijcai.org/Proceedings/2020/559) \n\n \t\t* #### TACL-19\n\t\t\t[Semantic neural machine translation using AMR](https://www.aclweb.org/anthology/Q19-1002.pdf) \n\n \t\t\t[Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/Q19-1019/) \n\n \t\t* #### NAACL-18\n\t\t\t[Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks](https://www.aclweb.org/anthology/N18-2078/) \n\n \t\t* #### EACL-17\n\t\t\t[Context-Aware Graph Segmentation for Graph-Based Translation](https://www.aclweb.org/anthology/E17-2095.pdf) \n\n \t- ### GNN for Undirected Graphs\n\t\t* #### ACL-20\n\t\t\t[A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://www.aclweb.org/anthology/2020.acl-main.273/) \n\n \t\t* #### TACL-19\n\t\t\t[Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning](https://www.aclweb.org/anthology/Q19-1019/) \n\n \t- ### Knowledge\n\t\t* #### COLING-20\n\t\t\t[Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity](https://www.aclweb.org/anthology/2020.coling-main.397/) \n\n \t\t* #### NAACL-19\n\t\t\t[Text Generation from Knowledge Graphs with Graph Transformers](https://www.aclweb.org/anthology/N19-1238/) \n\n \t\t* #### IJCAI-20\n\t\t\t[Knowledge Graphs Enhanced Neural Machine Translation](https://www.ijcai.org/Proceedings/2020/559) \n\n - ## Knowledge Base Completion/Reasoning\n\t- ### GNN for Directed Graphs\n\t\t* #### ICLR-20\n\t\t\t[DYNAMICALLY PRUNED MESSAGE PASSING NETWORKS FOR LARGE-SCALE KNOWLEDGE GRAPH\nREASONING](https://openreview.net/pdf?id=rkeuAhVKvB) \n\n \t- ### Knowledge\n\t\t* #### ICLR-20\n\t\t\t[DYNAMICALLY PRUNED MESSAGE PASSING NETWORKS FOR LARGE-SCALE KNOWLEDGE GRAPH\nREASONING](https://openreview.net/pdf?id=rkeuAhVKvB) \n\n - ## Knowledge Base Question Answering\n\t- ### GNN for Heterogeneous Graphs\n\t\t* #### EMNLP-20\n\t\t\t[Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.99/) \n\n \t\t* #### COLING-18\n\t\t\t[Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering](https://www.aclweb.org/anthology/C18-1280/) \n\n \t- ### Knowledge\n\t\t* #### EMNLP-20\n\t\t\t[Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://www.aclweb.org/anthology/2020.emnlp-main.99/) \n\n \t\t* #### COLING-18\n\t\t\t[Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering](https://www.aclweb.org/anthology/C18-1280/) \n\n ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgraph4ai%2Fgraph4nlp_literature","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgraph4ai%2Fgraph4nlp_literature","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgraph4ai%2Fgraph4nlp_literature/lists"}