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Resources, datasets, papers on Question Answering
https://github.com/dapurv5/awesome-question-answering

List: awesome-question-answering

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Resources, datasets, papers on Question Answering

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README 

        


    
Awesome Question Answering


    




A curated list of awesome question answering related resources, including papers, datasets, etc..



#### Papers 

- [Memory Networks](http://arxiv.org/pdf/1410.3916v11.pdf)

- [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895)

- [Towards AI-Complete Question Answering: A set of prerequisite toy tasks](http://arxiv.org/pdf/1502.05698v10.pdf)

- [Large Scale simple question answering with Memory Networks](https://arxiv.org/pdf/1506.02075v1.pdf)

- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](http://arxiv.org/pdf/1506.07285v5.pdf)

- [Key-Value Memory Networks for directly understanding documents](https://arxiv.org/pdf/1606.03126v1.pdf)

- [Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ACL15-STAGG.pdf)

- [Value of Semantic Parse Labeling for KBQA](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/acl2016-webqsp.pdf)

- [Question Answering with Subgraph Embeddings](https://arxiv.org/pdf/1406.3676v3.pdf)

- [Open Question Answering with Weakly Supervised Embedding Models](https://arxiv.org/pdf/1404.4326.pdf)

- [Learning End-to-End Goal-Oriented dialog](https://arxiv.org/pdf/1605.07683v2.pdf)

- [End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/IS16_ContextualSLU.pdf)

- [Question Answering over Knowledge Base With Neural Attention Combining Global Knowledge Information](https://arxiv.org/pdf/1606.00979v1.pdf)

- [Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Texts](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/acl2016relationpaths-1.pdf)

- [Neural Machine Translation by jointly learning to align and translate](https://arxiv.org/pdf/1409.0473v7.pdf)

- [Recurrent Neural Network Grammar](https://arxiv.org/pdf/1602.07776v4.pdf)

- [Neural Turing Machines](https://www.youtube.com/watch?v=_H0i0IhEO2g)

- [Teaching machines to read and comprehend](https://arxiv.org/pdf/1506.03340.pdf)

- [Applying Deep Learning to answer selection: A study and an open task](https://arxiv.org/pdf/1508.01585v2.pdf)

- [Reasoning with Neural Tensor Networks](https://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf)

- [Scalable Feature Learning for networks: Node2Vec](https://cs.stanford.edu/people/jure/pubs/node2vec-kdd16.pdf)

- [Learning Distributed Representations for Rooted Subgraphs from Large Graphs: Subgraph2Vec](https://arxiv.org/pdf/1606.08928.pdf)

- [Hybrid computing using a neural network with dynamic external memory](http://www.nature.com/nature/journal/v538/n7626/full/nature20101.html)

- [Traversing Knowledge Graphs in Vector Space](http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP038.pdf)

- [Learning to Compose Neural Networks for Question Answering](https://arxiv.org/abs/1601.01705)

- [Hierarchical Memory Networks](http://openreview.net/pdf?id=BJ0Ee8cxx)

- [Gaussian Attention Model and its Application to Knowledge Base Embedding and Question Answering](https://arxiv.org/pdf/1611.02266.pdf)

- [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493)

- [Sequence to Sequence Learning With Neural Networks](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)

- [Neural Conversation Model](https://arxiv.org/pdf/1506.05869v1.pdf)

- [Query Reduction Networks For Question Answering](https://arxiv.org/pdf/1606.04582.pdf)

- [Conditional Focused Neural Question Answering with Large-scale Knowledge Bases](https://arxiv.org/pdf/1606.01994.pdf)

- [Efficiently Answering Technical Questions — A Knowledge Graph Approach](http://wangzhongyuan.com/en/papers/Technical_Questions_Answering.pdf)

- [An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge](http://www.nlpr.ia.ac.cn/cip/~liukang/liukangPageFile/ACL2017-Hao.pdf)

- [Question Answering as Global Reasoning over Semantic Abstractions](http://www.cis.upenn.edu/~danielkh/files/2018_semanticilp/2018_aaai_semanticilp.pdf)



#### Category

##### Question generation

- [Question Generation via Overgenerating Transformations and Ranking (Technical report)](https://www.lti.cs.cmu.edu/sites/default/files/cmulti09013.pdf)

- [Automation of question generation from sentences](http://www.sadidhasan.com/sadid-QG.pdf)

- [Good question!statistical ranking for question generation](https://homes.cs.washington.edu/~nasmith/papers/heilman+smith.naacl10.pdf)

- [Question generation from paragraphs at upenn: Qgstec system description](http://www.aclweb.org/anthology/I11-1104)

- [Automatically generating questions from queries for community-based question answering](http://www.aclweb.org/anthology/I11-1104)

- [How to Generate Cloze Questions from Definitions: A Syntactic Approach](https://www.cs.cmu.edu/~listen/pdfs/gates-2011-aaai-qg.pdf)

- [Generating natural language questions to support learning on-line](http://www.aclweb.org/anthology/W13-2114)

- [Deep questions without deep understanding](http://www.aclweb.org/anthology/P15-1086)

- [Leveraging multiple views of text for automatic question generation](http://link.springer.com/chapter/10.1007/978-3-319-19773-9_26)

- [Revup: Automatic gap-fill question generation from educational texts](http://www.aclweb.org/anthology/W15-0618)

- [Towards topic-to-question generation](http://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00206)

- [Ranking automatically generated questions using common human queries](http://www.aclweb.org/old_anthology/W/W16/W16-66.pdf#page=233)

- [Generating quiz questions from knowledge graphs](http://delivery.acm.org/10.1145/2750000/2742722/p113-seyler.pdf)

- [Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus](http://arxiv.org/pdf/1603.06807v1.pdf)

- [Knowledge Questions from Knowledge Graphs](https://arxiv.org/abs/1610.09935)

- [Machine Comprehension by Text-to-Text Neural Question Generation](http://aclweb.org/anthology/W17-2603)

- [Question Generation from a Knowledge Base with Web Exploration](https://arxiv.org/pdf/1610.03807.pdf)

- [On Generating Characteristic-rich Question Sets for QA Evaluation](http://www.aclweb.org/anthology/D/D16/D16-1054.pdf)

- [Neural Question Generation from Text: A Preliminary Study](https://arxiv.org/pdf/1704.01792.pdf)

- [Semi-supervised qa with generative domain-adaptive nets](https://pdfs.semanticscholar.org/e8a0/536dc080acd2ca83502dddd0d511ef3fbd8c.pdf)



#### Datasets

- [bAbI dataset](https://research.facebook.com/research/babi/)

- [CNN QA Task (Teaching Machines to Read & Comprehend)](https://github.com/deepmind/rc-data/)

- [WebQuestions](http://nlp.stanford.edu/software/sempre/)

- [Simple Questions](https://research.facebook.com/research/babi)

- [Movie QA](https://research.facebook.com/research/babi/)

- [WebQuestionsSP](https://www.microsoft.com/en-us/download/details.aspx?id=52763)

- [WikiQA](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/YangYihMeek_EMNLP-15_WikiQA.pdf)

- [Kaggle AllenAI Challenge](https://www.kaggle.com/c/the-allen-ai-science-challenge)

- [MC Test, Machine Comprehension Test Microsoft 2013](http://research.microsoft.com/en-us/um/redmond/projects/mctest/)

- [MSR Sentence Completion Challenge](https://www.microsoft.com/en-us/research/project/msr-sentence-completion-challenge/)

- [Dialog State Tracking Challenge](http://camdial.org/~mh521/dstc/)

- [QA dataset featured in Teaching Machines to Read and Comprehend](https://github.com/deepmind/rc-data/)

- [WebNav](https://github.com/nyu-dl/WebNav/blob/master/README.md)

- [Stanford Question Answering Dataset](https://rajpurkar.github.io/SQuAD-explorer/)

- [FB15K Knowledge Base](https://www.microsoft.com/en-us/download/details.aspx?id=52312)

- Yahoo! Answers Comprehensive Questions and Answers version 1.0 (multi part)

- [Cornell Movie Dialogue Dataset](https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html)

- [WikiQA](http://aka.ms/WikiQA)

- [Quora Duplicate Questions Dataset](https://data.quora.com/)

- [Query Reformulator Dataset Jeopardy etc](https://github.com/nyu-dl/QueryReformulator)

- [Quiz Bowl Questions](https://www.cs.colorado.edu/~jbg/projects/IIS-1320538.html#Datasets)

- [WebQA-Chinese](http://idl.baidu.com/WebQA.html)

- [Chat corpus](https://github.com/Marsan-Ma/chat_corpus)

- [MultiRC](http://cogcomp.org/multirc/)

- [NewsQA](https://github.com/Maluuba/newsqa)



#### KBs

- [NetBase](https://github.com/pannous/netbase)

- [Freebase](https://developers.google.com/freebase/)



#### Presentations

- [Relation Learning for Large Scale Knowledge Graph](http://nlp.csai.tsinghua.edu.cn/~lzy/talks/adl2015.pdf)

- [Attention and Memory](http://videolectures.net/site/normal_dl/tag=1051694/deeplearning2016_chopra_attention_memory_01.pdf)



#### LM Datasets

- PennTree Bank

- Text8



#### Code & Relevant Projects

- [MemNN Impl Matlab](https://github.com/facebook/MemNN)

- [Key Value MemNN](https://github.com/siyuanzhao/key-value-memory-networks)

- [Quepy](https://github.com/machinalis/quepy)

- [NLQuery](https://github.com/ayoungprogrammer/nlquery)

- [ParlAI](https://github.com/facebookresearch/ParlAI)

- [flask-chatterbot](https://github.com/chamkank/flask-chatterbot)

- [Learning to Rank short text pairs with CNN SIGIR 2015](https://github.com/shashankg7/Keras-CNN-QA)

- [TextKBQA](https://github.com/rajarshd/TextKBQA)

- [BiAttnFlow](https://github.com/allenai/bi-att-flow)