awesome-qa
NLP QA
A curated list of question answering systems and techniques in natural language processing
😎 A curated list of the Question Answering (QA)
755 stars
39 watching
105 forks
last commit: almost 3 years ago
Linked from 3 awesome lists
awesomeawesome-listbertdeepqamachine-comprehensionnlpquestion-answeringsquadwatson
Awesome Question Answering / Recent Trends / Recent QA Models / DilBert: Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering (2020) | |||
| https://arxiv.org/pdf/2010.08422.pdf | paper: | ||
| https://github.com/wissam-sib/dilbert | 16 | about 4 years ago | github: |
Awesome Question Answering / Recent Trends / Recent QA Models / UnifiedQA: Crossing Format Boundaries With a Single QA System (2020) | |||
| https://unifiedqa.apps.allenai.org/ | Demo: | ||
Awesome Question Answering / Recent Trends / Recent QA Models / ProQA: Resource-efficient method for pretraining a dense corpus index for open-domain QA and IR. (2020) | |||
| https://arxiv.org/pdf/2005.00038.pdf | paper: | ||
| https://github.com/xwhan/ProQA | 43 | over 1 year ago | github: |
Awesome Question Answering / Recent Trends / Recent QA Models / TYDI QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020) | |||
| https://arxiv.org/ftp/arxiv/papers/2003/2003.05002.pdf | paper: | ||
Awesome Question Answering / Recent Trends / Recent QA Models / Retrospective Reader for Machine Reading Comprehension | |||
| https://arxiv.org/pdf/2001.09694v2.pdf | paper: | ||
Awesome Question Answering / Recent Trends / Recent QA Models / TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection (AAAI 2020) | |||
| https://arxiv.org/pdf/1911.04118.pdf | paper: | ||
Awesome Question Answering / Recent Trends / Recent Language Models | |||
| ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators | , Kevin Clark, et al., ICLR, 2020 | ||
| TinyBERT: Distilling BERT for Natural Language Understanding | , Xiaoqi Jiao, et al., ICLR, 2020 | ||
| MINILM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers | , Wenhui Wang, et al., arXiv, 2020 | ||
| T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | , Colin Raffel, et al., arXiv preprint, 2019 | ||
| ERNIE: Enhanced Language Representation with Informative Entities | , Zhengyan Zhang, et al., ACL, 2019 | ||
| XLNet: Generalized Autoregressive Pretraining for Language Understanding | , Zhilin Yang, et al., arXiv preprint, 2019 | ||
| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations | , Zhenzhong Lan, et al., arXiv preprint, 2019 | ||
| RoBERTa: A Robustly Optimized BERT Pretraining Approach | , Yinhan Liu, et al., arXiv preprint, 2019 | ||
| DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter | , Victor sanh, et al., arXiv, 2019 | ||
| SpanBERT: Improving Pre-training by Representing and Predicting Spans | , Mandar Joshi, et al., TACL, 2019 | ||
| BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | , Jacob Devlin, et al., NAACL 2019, 2018 | ||
Awesome Question Answering / Recent Trends / AAAI 2020 | |||
| TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection | , Siddhant Garg, et al., AAAI 2020, Nov 2019 | ||
Awesome Question Answering / Recent Trends / ACL 2019 | |||
| Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering | , Asma Ben Abacha, et al., ACL-W 2019, Aug 2019 | ||
| Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications | , Wei Zhao, et al., ACL 2019, Jun 2019 | ||
| Cognitive Graph for Multi-Hop Reading Comprehension at Scale | , Ming Ding, et al., ACL 2019, Jun 2019 | ||
| Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index | , Minjoon Seo, et al., ACL 2019, Jun 2019 | ||
| Unsupervised Question Answering by Cloze Translation | , Patrick Lewis, et al., ACL 2019, Jun 2019 | ||
| SemEval-2019 Task 10: Math Question Answering | , Mark Hopkins, et al., ACL-W 2019, Jun 2019 | ||
| Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader | , Wenhan Xiong, et al., ACL 2019, May 2019 | ||
| Matching Article Pairs with Graphical Decomposition and Convolutions | , Bang Liu, et al., ACL 2019, May 2019 | ||
| Episodic Memory Reader: Learning what to Remember for Question Answering from Streaming Data | , Moonsu Han, et al., ACL 2019, Mar 2019 | ||
| Natural Questions: a Benchmark for Question Answering Research | , Tom Kwiatkowski, et al., TACL 2019, Jan 2019 | ||
| Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension | , Daesik Kim, et al., ACL 2019, Nov 2018 | ||
Awesome Question Answering / Recent Trends / EMNLP-IJCNLP 2019 | |||
| Language Models as Knowledge Bases? | , Fabio Petron, et al., EMNLP-IJCNLP 2019, Sep 2019 | ||
| LXMERT: Learning Cross-Modality Encoder Representations from Transformers | , Hao Tan, et al., EMNLP-IJCNLP 2019, Dec 2019 | ||
| Answering Complex Open-domain Questions Through Iterative Query Generation | , Peng Qi, et al., EMNLP-IJCNLP 2019, Oct 2019 | ||
| KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning | , Bill Yuchen Lin, et al., EMNLP-IJCNLP 2019, Sep 2019 | ||
| Mixture Content Selection for Diverse Sequence Generation | , Jaemin Cho, et al., EMNLP-IJCNLP 2019, Sep 2019 | ||
| A Discrete Hard EM Approach for Weakly Supervised Question Answering | , Sewon Min, et al., EMNLP-IJCNLP, 2019, Sep 2019 | ||
Awesome Question Answering / Recent Trends / Arxiv | |||
| Investigating the Successes and Failures of BERT for Passage Re-Ranking | , Harshith Padigela, et al., arXiv preprint, May 2019 | ||
| BERT with History Answer Embedding for Conversational Question Answering | , Chen Qu, et al., arXiv preprint, May 2019 | ||
| Understanding the Behaviors of BERT in Ranking | , Yifan Qiao, et al., arXiv preprint, Apr 2019 | ||
| BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis | , Hu Xu, et al., arXiv preprint, Apr 2019 | ||
| End-to-End Open-Domain Question Answering with BERTserini | , Wei Yang, et al., arXiv preprint, Feb 2019 | ||
| A BERT Baseline for the Natural Questions | , Chris Alberti, et al., arXiv preprint, Jan 2019 | ||
| Passage Re-ranking with BERT | , Rodrigo Nogueira, et al., arXiv preprint, Jan 2019 | ||
| SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering | , Chenguang Zhu, et al., arXiv, Dec 2018 | ||
Awesome Question Answering / Recent Trends / Dataset | |||
| ELI5: Long Form Question Answering | , Angela Fan, et al., ACL 2019, Jul 2019 | ||
| CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense | , Michael Chen, et al., RepEval 2019, Jun 2019 | ||
Awesome Question Answering / About QA / Analysis and Parsing for Pre-processing in QA systems | |||
| Morphological analysis | |||
| Named Entity Recognition(NER) | |||
Awesome Question Answering / Systems | |||
| IBM Watson | Has state-of-the-arts performance | ||
| Facebook DrQA | Applied to the SQuAD1.0 dataset. The SQuAD2.0 dataset has released. but DrQA is not tested yet | ||
| MIT media lab's Knowledge graph | Is a freely-available semantic network, designed to help computers understand the meanings of words that people use | ||
Awesome Question Answering / Competitions in QA | |||
| Story Cloze Test | |||
| SQuAD | |||
| SQuAD 2.0 | |||
| TriviaQA | |||
| decaNLP | |||
| DuReader Ver1. | |||
| DuReader Ver2. | |||
| KorQuAD | |||
| KorQuAD 2.0 | |||
| CoQA | |||
Awesome Question Answering / Publications / Papers | |||
| "Learning to Skim Text" | , Adams Wei Yu, Hongrae Lee, Quoc V. Le, 2017. : Show only what you want in Text | ||
| "Deep Joint Entity Disambiguation with Local Neural Attention" | , Octavian-Eugen Ganea and Thomas Hofmann, 2017 | ||
| "BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION" | , Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hananneh Hajishirzi, ICLR, 2017 | ||
| "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks" | , Matthew Francis-Landau, Greg Durrett and Dan Klei, NAACL-HLT 2016 | ||
Awesome Question Answering / Publications / Papers / "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks" | |||
| https://GitHub.com/matthewfl/nlp-entity-convnet | |||
Awesome Question Answering / Publications / Papers | |||
| "Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions" | , Wei Shen, Jianyong Wang, Jiawei Han, IEEE Transactions on Knowledge and Data Engineering(TKDE), 2014 | ||
| "Introduction to “This is Watson" | , IBM Journal of Research and Development, D. A. Ferrucci, 2012 | ||
| "A survey on question answering technology from an information retrieval perspective" | , Information Sciences, 2011 | ||
| "Question Answering in Restricted Domains: An Overview" | , Diego Mollá and José Luis Vicedo, Computational Linguistics, 2007 | ||
| "Natural language question answering: the view from here" | , L Hirschman, R Gaizauskas, natural language engineering, 2001 | ||
Awesome Question Answering / Codes | |||
| BiDAF | 1,535 | over 1 year ago | Bi-Directional Attention Flow (BIDAF) network is a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization |
Awesome Question Answering / Codes / BiDAF | |||
| Paper | |||
Awesome Question Answering / Codes | |||
| QANet | 983 | over 6 years ago | A Q&A architecture does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions |
| R-Net | 578 | over 6 years ago | An end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage |
Awesome Question Answering / Codes / R-Net | |||
| Paper | |||
Awesome Question Answering / Codes | |||
| R-Net-in-Keras | 178 | almost 7 years ago | R-NET re-implementation in Keras |
Awesome Question Answering / Codes / R-Net-in-Keras | |||
| Paper | |||
Awesome Question Answering / Codes | |||
| DrQA | 401 | over 2 years ago | DrQA is a system for reading comprehension applied to open-domain question answering |
| BERT | 38,204 | 4 months ago | A new language representation model which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers |
Awesome Question Answering / Codes / BERT | |||
| Paper | |||
Awesome Question Answering / Lectures | |||
| Question Answering - Natural Language Processing | By Dragomir Radev, Ph.D. | University of Michigan | 2016 | ||
Awesome Question Answering / Slides | |||
| Question Answering with Knowledge Bases, Web and Beyond | 34 | over 5 years ago | By Scott Wen-tau Yih & Hao Ma | Microsoft Research | 2016 |
| Question Answering | By Dr. Mariana Neves | Hasso Plattner Institut | 2017 | ||
Awesome Question Answering / Dataset Collections | |||
| NLIWOD's Question answering datasets | 94 | over 2 years ago | |
| karthinkncode's Datasets for Natural Language Processing | 919 | almost 5 years ago | |
Awesome Question Answering / Datasets | |||
| AI2 Science Questions v2.1(2017) | |||
Awesome Question Answering / Datasets / AI2 Science Questions v2.1(2017) | |||
| http://ai2-website.s3.amazonaws.com/publications/AI2ReasoningChallenge2018.pdf | Paper: | ||
Awesome Question Answering / Datasets | |||
| Children's Book Test | |||
| CODAH Dataset | 22 | about 2 years ago | |
| DeepMind Q&A Dataset; CNN/Daily Mail | 1,293 | over 7 years ago | |
Awesome Question Answering / Datasets / DeepMind Q&A Dataset; CNN/Daily Mail | |||
| https://arxiv.org/abs/1506.03340 | Paper: | ||
Awesome Question Answering / Datasets | |||
| ELI5 | 319 | about 3 years ago | |
Awesome Question Answering / Datasets / ELI5 | |||
| https://arxiv.org/abs/1907.09190 | Paper: | ||
Awesome Question Answering / Datasets | |||
| GraphQuestions | 92 | almost 2 years ago | |
| LC-QuAD | |||
| MS MARCO | |||
Awesome Question Answering / Datasets / MS MARCO | |||
| https://arxiv.org/abs/1611.09268 | Paper: | ||
Awesome Question Answering / Datasets | |||
| MultiRC | |||
Awesome Question Answering / Datasets / MultiRC | |||
| http://cogcomp.org/page/publication_view/833 | Paper: | ||
Awesome Question Answering / Datasets | |||
| NarrativeQA | 458 | over 4 years ago | |
Awesome Question Answering / Datasets / NarrativeQA | |||
| https://arxiv.org/pdf/1712.07040v1.pdf | Paper: | ||
Awesome Question Answering / Datasets | |||
| NewsQA | 253 | almost 2 years ago | |
Awesome Question Answering / Datasets / NewsQA | |||
| https://arxiv.org/pdf/1611.09830.pdf | Paper: | ||
Awesome Question Answering / Datasets | |||
| Qestion-Answer Dataset by CMU | |||
| SQuAD1.0 | |||
Awesome Question Answering / Datasets / SQuAD1.0 | |||
| https://arxiv.org/abs/1606.05250 | Paper: | ||
Awesome Question Answering / Datasets | |||
| SQuAD2.0 | |||
Awesome Question Answering / Datasets / SQuAD2.0 | |||
| https://arxiv.org/abs/1806.03822 | Paper: | ||
Awesome Question Answering / Datasets | |||
| Story cloze test | |||
Awesome Question Answering / Datasets / Story cloze test | |||
| https://arxiv.org/abs/1604.01696 | Paper: | ||
Awesome Question Answering / Datasets | |||
| TriviaQA | |||
Awesome Question Answering / Datasets / TriviaQA | |||
| https://arxiv.org/abs/1705.03551 | Paper: | ||
Awesome Question Answering / Datasets | |||
| WikiQA | |||
Awesome Question Answering / Datasets / The DeepQA Research Team in IBM Watson's publication within 5 years / 2015 | |||
| "Unsupervised Entity-Relation Analysis in IBM Watson" | , Aditya Kalyanpur, J William Murdock, ACS, 2015 | ||
Awesome Question Answering / Datasets / The DeepQA Research Team in IBM Watson's publication within 5 years / 2014 | |||
| "WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information" | , Adam Lally, Sugato Bachi, Michael A. Barborak, David W. Buchanan, Jennifer Chu-Carroll, David A. Ferrucci*, Michael R. Glass, Aditya Kalyanpur, Erik T. Mueller, J. William Murdock, Siddharth Patwardhan, John M. Prager, Christopher A. Welty, IBM Research Report RC25489, 2014 | ||
| "Medical Relation Extraction with Manifold Models" | , Chang Wang and James Fan, ACL, 2014 | ||
Awesome Question Answering / Datasets / MS Research's publication within 5 years / 2018 | |||
| "FigureQA: An Annotated Figure Dataset for Visual Reasoning" | , Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Akos Kadar, Adam Trischler, Yoshua Bengio, ICLR, 2018 | ||
Awesome Question Answering / Datasets / MS Research's publication within 5 years / 2016 | |||
| "Stacked Attention Networks for Image Question Answering" | , Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, CVPR, 2016 | ||
| "Question Answering with Knowledge Base, Web and Beyond" | , Yih, Scott Wen-tau and Ma, Hao, ACM SIGIR, 2016 | ||
| "NewsQA: A Machine Comprehension Dataset" | , Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman, RepL4NLP, 2016 | ||
| "Table Cell Search for Question Answering" | , Sun, Huan and Ma, Hao and He, Xiaodong and Yih, Wen-tau and Su, Yu and Yan, Xifeng, WWW, 2016 | ||
Awesome Question Answering / Datasets / MS Research's publication within 5 years / 2015 | |||
| "WIKIQA: A Challenge Dataset for Open-Domain Question Answering" | , Yi Yang, Wen-tau Yih, and Christopher Meek, EMNLP, 2015 | ||
| "Web-based Question Answering: Revisiting AskMSR" | , Chen-Tse Tsai, Wen-tau Yih, and Christopher J.C. Burges, MSR-TR, 2015 | ||
| "Open Domain Question Answering via Semantic Enrichment" | , Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, and Ming-Wei Chang, WWW, 2015 | ||
Awesome Question Answering / Datasets / MS Research's publication within 5 years / 2014 | |||
| "An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark" | , Zhenghao Wang, Shengquan Yan, Huaming Wang, and Xuedong Huang, MSR-TR, 2014 | ||
| "Semantic Parsing for Single-Relation Question Answering" | , Wen-tau Yih, Xiaodong He, Christopher Meek, ACL, 2014 | ||
Awesome Question Answering / Datasets / Google AI's publication within 5 years / 2018 | |||
| Google QA | |||
| "QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension" | , Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le, ICLR, 2018 | ||
| "Ask the Right Questions: Active Question Reformulation with Reinforcement Learning" | , Christian Buck and Jannis Bulian and Massimiliano Ciaramita and Wojciech Paweł Gajewski and Andrea Gesmundo and Neil Houlsby and Wei Wang, ICLR, 2018 | ||
| "Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors" | , Radu Soricut, Nan Ding, 2018 | ||
Awesome Question Answering / Datasets / Google AI's publication within 5 years / 2018 / Sentence representation | |||
| "An efficient framework for learning sentence representations" | , Lajanugen Logeswaran, Honglak Lee, ICLR, 2018 | ||
Awesome Question Answering / Datasets / Google AI's publication within 5 years / 2018 | |||
| "Did the model understand the question?" | , Pramod K. Mudrakarta and Ankur Taly and Mukund Sundararajan and Kedar Dhamdhere, ACL, 2018 | ||
Awesome Question Answering / Datasets / Google AI's publication within 5 years / 2017 | |||
| "Analyzing Language Learned by an Active Question Answering Agent" | , Christian Buck and Jannis Bulian and Massimiliano Ciaramita and Wojciech Gajewski and Andrea Gesmundo and Neil Houlsby and Wei Wang, NIPS, 2017 | ||
| "Learning Recurrent Span Representations for Extractive Question Answering" | , Kenton Lee and Shimi Salant and Tom Kwiatkowski and Ankur Parikh and Dipanjan Das and Jonathan Berant, ICLR, 2017 | ||
Awesome Question Answering / Datasets / Google AI's publication within 5 years / 2017 / Identify the same question | |||
| "Neural Paraphrase Identification of Questions with Noisy Pretraining" | , Gaurav Singh Tomar and Thyago Duque and Oscar Täckström and Jakob Uszkoreit and Dipanjan Das, SCLeM, 2017 | ||
Awesome Question Answering / Datasets / Facebook AI Research's publication within 5 years / 2018 | |||
| Embodied Question Answering | , Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra, CVPR, 2018 | ||
| Do explanations make VQA models more predictable to a human? | , Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh, EMNLP, 2018 | ||
| Neural Compositional Denotational Semantics for Question Answering | , Nitish Gupta, Mike Lewis, EMNLP, 2018 | ||
Awesome Question Answering / Datasets / Facebook AI Research's publication within 5 years / 2017 | |||
| DrQA | |||
| Reading Wikipedia to Answer Open-Domain Questions | , Danqi Chen, Adam Fisch, Jason Weston & Antoine Bordes, ACL, 2017 | ||
Awesome Question Answering / Links | |||
| Building a Question-Answering System from Scratch— Part 1 | |||
| Qeustion Answering with Tensorflow By Steven Hewitt, O'REILLY, 2017 | |||
| Why question answering is hard | |||
sindresorhus/awesome
jnv/lists
0ex/more-awesome