{"id":13444393,"url":"https://github.com/dapurv5/awesome-question-answering","last_synced_at":"2025-09-16T05:31:55.502Z","repository":{"id":12849608,"uuid":"68793962","full_name":"dapurv5/awesome-question-answering","owner":"dapurv5","description":"Resources, datasets, papers on Question Answering","archived":false,"fork":false,"pushed_at":"2023-03-17T19:52:11.000Z","size":27,"stargazers_count":681,"open_issues_count":0,"forks_count":194,"subscribers_count":47,"default_branch":"master","last_synced_at":"2025-09-03T14:02:05.859Z","etag":null,"topics":["awesome","awesome-list","deep-learning","machine-learning","papers","question-answering"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dapurv5.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2016-09-21T07:58:53.000Z","updated_at":"2025-08-16T23:14:11.000Z","dependencies_parsed_at":"2024-01-16T15:49:46.547Z","dependency_job_id":null,"html_url":"https://github.com/dapurv5/awesome-question-answering","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dapurv5/awesome-question-answering","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dapurv5%2Fawesome-question-answering","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dapurv5%2Fawesome-question-answering/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dapurv5%2Fawesome-question-answering/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dapurv5%2Fawesome-question-answering/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dapurv5","download_url":"https://codeload.github.com/dapurv5/awesome-question-answering/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dapurv5%2Fawesome-question-answering/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274398510,"owners_count":25277495,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-10T02:00:12.551Z","response_time":83,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["awesome","awesome-list","deep-learning","machine-learning","papers","question-answering"],"created_at":"2024-07-31T04:00:21.702Z","updated_at":"2025-09-16T05:31:55.401Z","avatar_url":"https://github.com/dapurv5.png","language":null,"funding_links":[],"categories":["Uncategorized","Natural Language Processing","Others","Other Lists"],"sub_categories":["Uncategorized","TeX Lists"],"readme":"\u003cdiv align=\"center\"\u003e\n    \u003ch1\u003eAwesome Question Answering\u003c/h1\u003e\n    \u003ca href=\"https://github.com/sindresorhus/awesome\"\u003e\u003cimg src=\"https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg\"/\u003e\u003c/a\u003e\n\u003c/div\u003e\n\nA curated list of awesome question answering related resources, including papers, datasets, etc..\n\n#### Papers \n- [Memory Networks](http://arxiv.org/pdf/1410.3916v11.pdf)\n- [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895)\n- [Towards AI-Complete Question Answering: A set of prerequisite toy tasks](http://arxiv.org/pdf/1502.05698v10.pdf)\n- [Large Scale simple question answering with Memory Networks](https://arxiv.org/pdf/1506.02075v1.pdf)\n- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](http://arxiv.org/pdf/1506.07285v5.pdf)\n- [Key-Value Memory Networks for directly understanding documents](https://arxiv.org/pdf/1606.03126v1.pdf)\n- [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)\n- [Value of Semantic Parse Labeling for KBQA](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/acl2016-webqsp.pdf)\n- [Question Answering with Subgraph Embeddings](https://arxiv.org/pdf/1406.3676v3.pdf)\n- [Open Question Answering with Weakly Supervised Embedding Models](https://arxiv.org/pdf/1404.4326.pdf)\n- [Learning End-to-End Goal-Oriented dialog](https://arxiv.org/pdf/1605.07683v2.pdf)\n- [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)\n- [Question Answering over Knowledge Base With Neural Attention Combining Global Knowledge Information](https://arxiv.org/pdf/1606.00979v1.pdf)\n- [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)\n- [Neural Machine Translation by jointly learning to align and translate](https://arxiv.org/pdf/1409.0473v7.pdf)\n- [Recurrent Neural Network Grammar](https://arxiv.org/pdf/1602.07776v4.pdf)\n- [Neural Turing Machines](https://www.youtube.com/watch?v=_H0i0IhEO2g)\n- [Teaching machines to read and comprehend](https://arxiv.org/pdf/1506.03340.pdf)\n- [Applying Deep Learning to answer selection: A study and an open task](https://arxiv.org/pdf/1508.01585v2.pdf)\n- [Reasoning with Neural Tensor Networks](https://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf)\n- [Scalable Feature Learning for networks: Node2Vec](https://cs.stanford.edu/people/jure/pubs/node2vec-kdd16.pdf)\n- [Learning Distributed Representations for Rooted Subgraphs from Large Graphs: Subgraph2Vec](https://arxiv.org/pdf/1606.08928.pdf)\n- [Hybrid computing using a neural network with dynamic external memory](http://www.nature.com/nature/journal/v538/n7626/full/nature20101.html)\n- [Traversing Knowledge Graphs in Vector Space](http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP038.pdf)\n- [Learning to Compose Neural Networks for Question Answering](https://arxiv.org/abs/1601.01705)\n- [Hierarchical Memory Networks](http://openreview.net/pdf?id=BJ0Ee8cxx)\n- [Gaussian Attention Model and its Application to Knowledge Base Embedding and Question Answering](https://arxiv.org/pdf/1611.02266.pdf)\n- [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493)\n- [Sequence to Sequence Learning With Neural Networks](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)\n- [Neural Conversation Model](https://arxiv.org/pdf/1506.05869v1.pdf)\n- [Query Reduction Networks For Question Answering](https://arxiv.org/pdf/1606.04582.pdf)\n- [Conditional Focused Neural Question Answering with Large-scale Knowledge Bases](https://arxiv.org/pdf/1606.01994.pdf)\n- [Efficiently Answering Technical Questions — A Knowledge Graph Approach](http://wangzhongyuan.com/en/papers/Technical_Questions_Answering.pdf)\n- [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)\n- [Question Answering as Global Reasoning over Semantic Abstractions](http://www.cis.upenn.edu/~danielkh/files/2018_semanticilp/2018_aaai_semanticilp.pdf)\n\n#### Category\n##### Question generation\n- [Question Generation via Overgenerating Transformations and Ranking (Technical report)](https://www.lti.cs.cmu.edu/sites/default/files/cmulti09013.pdf)\n- [Automation of question generation from sentences](http://www.sadidhasan.com/sadid-QG.pdf)\n- [Good question!statistical ranking for question generation](https://homes.cs.washington.edu/~nasmith/papers/heilman+smith.naacl10.pdf)\n- [Question generation from paragraphs at upenn: Qgstec system description](http://www.aclweb.org/anthology/I11-1104)\n- [Automatically generating questions from queries for community-based question answering](http://www.aclweb.org/anthology/I11-1104)\n- [How to Generate Cloze Questions from Definitions: A Syntactic Approach](https://www.cs.cmu.edu/~listen/pdfs/gates-2011-aaai-qg.pdf)\n- [Generating natural language questions to support learning on-line](http://www.aclweb.org/anthology/W13-2114)\n- [Deep questions without deep understanding](http://www.aclweb.org/anthology/P15-1086)\n- [Leveraging multiple views of text for automatic question generation](http://link.springer.com/chapter/10.1007/978-3-319-19773-9_26)\n- [Revup: Automatic gap-fill question generation from educational texts](http://www.aclweb.org/anthology/W15-0618)\n- [Towards topic-to-question generation](http://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00206)\n- [Ranking automatically generated questions using common human queries](http://www.aclweb.org/old_anthology/W/W16/W16-66.pdf#page=233)\n- [Generating quiz questions from knowledge graphs](http://delivery.acm.org/10.1145/2750000/2742722/p113-seyler.pdf)\n- [Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus](http://arxiv.org/pdf/1603.06807v1.pdf)\n- [Knowledge Questions from Knowledge Graphs](https://arxiv.org/abs/1610.09935)\n- [Machine Comprehension by Text-to-Text Neural Question Generation](http://aclweb.org/anthology/W17-2603)\n- [Question Generation from a Knowledge Base with Web Exploration](https://arxiv.org/pdf/1610.03807.pdf)\n- [On Generating Characteristic-rich Question Sets for QA Evaluation](http://www.aclweb.org/anthology/D/D16/D16-1054.pdf)\n- [Neural Question Generation from Text: A Preliminary Study](https://arxiv.org/pdf/1704.01792.pdf)\n- [Semi-supervised qa with generative domain-adaptive nets](https://pdfs.semanticscholar.org/e8a0/536dc080acd2ca83502dddd0d511ef3fbd8c.pdf)\n\n\n#### Datasets\n- [bAbI dataset](https://research.facebook.com/research/babi/)\n- [CNN QA Task (Teaching Machines to Read \u0026 Comprehend)](https://github.com/deepmind/rc-data/)\n- [WebQuestions](http://nlp.stanford.edu/software/sempre/)\n- [Simple Questions](https://research.facebook.com/research/babi)\n- [Movie QA](https://research.facebook.com/research/babi/)\n- [WebQuestionsSP](https://www.microsoft.com/en-us/download/details.aspx?id=52763)\n- [WikiQA](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/YangYihMeek_EMNLP-15_WikiQA.pdf)\n- [Kaggle AllenAI Challenge](https://www.kaggle.com/c/the-allen-ai-science-challenge)\n- [MC Test, Machine Comprehension Test Microsoft 2013](http://research.microsoft.com/en-us/um/redmond/projects/mctest/)\n- [MSR Sentence Completion Challenge](https://www.microsoft.com/en-us/research/project/msr-sentence-completion-challenge/)\n- [Dialog State Tracking Challenge](http://camdial.org/~mh521/dstc/)\n- [QA dataset featured in Teaching Machines to Read and Comprehend](https://github.com/deepmind/rc-data/)\n- [WebNav](https://github.com/nyu-dl/WebNav/blob/master/README.md)\n- [Stanford Question Answering Dataset](https://rajpurkar.github.io/SQuAD-explorer/)\n- [FB15K Knowledge Base](https://www.microsoft.com/en-us/download/details.aspx?id=52312)\n- Yahoo! Answers Comprehensive Questions and Answers version 1.0 (multi part)\n- [Cornell Movie Dialogue Dataset](https://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html)\n- [WikiQA](http://aka.ms/WikiQA)\n- [Quora Duplicate Questions Dataset](https://data.quora.com/)\n- [Query Reformulator Dataset Jeopardy etc](https://github.com/nyu-dl/QueryReformulator)\n- [Quiz Bowl Questions](https://www.cs.colorado.edu/~jbg/projects/IIS-1320538.html#Datasets)\n- [WebQA-Chinese](http://idl.baidu.com/WebQA.html)\n- [Chat corpus](https://github.com/Marsan-Ma/chat_corpus)\n- [MultiRC](http://cogcomp.org/multirc/)\n- [NewsQA](https://github.com/Maluuba/newsqa)\n\n#### KBs\n- [NetBase](https://github.com/pannous/netbase)\n- [Freebase](https://developers.google.com/freebase/)\n\n#### Presentations\n- [Relation Learning for Large Scale Knowledge Graph](http://nlp.csai.tsinghua.edu.cn/~lzy/talks/adl2015.pdf)\n- [Attention and Memory](http://videolectures.net/site/normal_dl/tag=1051694/deeplearning2016_chopra_attention_memory_01.pdf)\n\n#### LM Datasets\n- PennTree Bank\n- Text8\n\n#### Code \u0026 Relevant Projects\n- [MemNN Impl Matlab](https://github.com/facebook/MemNN)\n- [Key Value MemNN](https://github.com/siyuanzhao/key-value-memory-networks)\n- [Quepy](https://github.com/machinalis/quepy)\n- [NLQuery](https://github.com/ayoungprogrammer/nlquery)\n- [ParlAI](https://github.com/facebookresearch/ParlAI)\n- [flask-chatterbot](https://github.com/chamkank/flask-chatterbot)\n- [Learning to Rank short text pairs with CNN SIGIR 2015](https://github.com/shashankg7/Keras-CNN-QA)\n- [TextKBQA](https://github.com/rajarshd/TextKBQA)\n- [BiAttnFlow](https://github.com/allenai/bi-att-flow)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdapurv5%2Fawesome-question-answering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdapurv5%2Fawesome-question-answering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdapurv5%2Fawesome-question-answering/lists"}