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 Awesome Question Answering !Awesome (https://awesome.re/badge.svg) (https://github.com/sindresorhus/awesome) 
 Awesome Question Answering !Awesome (https://awesome.re/badge.svg) (https://github.com/sindresorhus/awesome) 
_A curated list of the __Question Answering (QA) (https://en.wikipedia.org/wiki/Question_answering)__ subject which is a computer science discipline within the fields of information retrieval and natural language processing (NLP) toward
using machine learning and deep learning_
_A curated list of the __Question Answering (QA) (https://en.wikipedia.org/wiki/Question_answering)__ subject which is a computer science discipline within the fields of information retrieval and natural language processing (NLP) toward using 
machine learning and deep learning_
_정보 검색 및 자연 언어 처리 분야의 질의응답에 관한 큐레이션 - 머신러닝과 딥러닝 단계까지_
_问答系统主题的精选列表，是信息检索和自然语言处理领域的计算机科学学科 - 使用机器学习和深度学习_
@@ -153,8 +153,8 @@
: Show only what you want in Text
 - "Deep Joint Entity Disambiguation with Local Neural Attention" (https://arxiv.org/pdf/1704.04920.pdf), Octavian-Eugen Ganea and Thomas Hofmann, 2017.
 - "BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION" (https://arxiv.org/pdf/1611.01603.pdf), Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hananneh Hajishirzi, ICLR, 2017.
 - "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks" (http://nlp.cs.berkeley.edu/pubs/FrancisLandau-Durrett-Klein_2016_EntityConvnets_paper.pdf), Matthew Francis-Landau, Greg Durrett and Dan Klei, 
NAACL-HLT 2016.
 - "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks" (http://nlp.cs.berkeley.edu/pubs/FrancisLandau-Durrett-Klein_2016_EntityConvnets_paper.pdf), Matthew Francis-Landau, Greg Durrett and Dan Klei, NAACL-HLT 
2016.
- https://GitHub.com/matthewfl/nlp-entity-convnet
 - "Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions" (https://ieeexplore.ieee.org/document/6823700/), Wei Shen, Jianyong Wang, Jiawei Han, IEEE Transactions on Knowledge and Data Engineering(TKDE), 2014.
 - "Introduction to “This is Watson" (https://ieeexplore.ieee.org/document/6177724/), IBM Journal of Research and Development, D. A. Ferrucci, 2012.
@@ -164,12 +164,12 @@
 - Entity Disambiguation / Entity Linking
Codes
- BiDAF (https://github.com/allenai/bi-att-flow) - 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. 
- BiDAF (https://github.com/allenai/bi-att-flow) - 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. 
 - Official; Tensorflow v1.2
 - Paper (https://arxiv.org/pdf/1611.01603.pdf)
- QANet (https://github.com/NLPLearn/QANet) - 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.
- QANet (https://github.com/NLPLearn/QANet) - 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.
 - Google; Unofficial; Tensorflow v1.5
 - Paper (#qanet)
- R-Net (https://github.com/HKUST-KnowComp/R-Net) - An end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage.
@@ -181,8 +181,8 @@
- DrQA (https://github.com/hitvoice/DrQA) - DrQA is a system for reading comprehension applied to open-domain question answering.
 - Facebook; Official; Pytorch v0.4
 - Paper (#drqa)
- BERT (https://github.com/google-research/bert) - 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. 
- BERT (https://github.com/google-research/bert) - 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. 
 - Google; Official implementation; Tensorflow v1.11.0
 - Paper (https://arxiv.org/abs/1810.04805)
@@ -202,12 +202,11 @@
 - It consists of questions used in student assessments in the United States across elementary and middle school grade levels. Each question is 4-way multiple choice format and may or may not include a diagram element.
 - Paper: http://ai2-website.s3.amazonaws.com/publications/AI2ReasoningChallenge2018.pdf
- Children's Book Test (https://uclmr.github.io/ai4exams/data.html)
- It is one of the bAbI project of Facebook AI Research which is organized towards the goal of automatic text understanding and reasoning. The CBT is designed to measure directly how well language models can exploit wider linguistic 
context.
- It is one of the bAbI project of Facebook AI Research which is organized towards the goal of automatic text understanding and reasoning. The CBT is designed to measure directly how well language models can exploit wider linguistic context.
- CODAH Dataset (https://github.com/Websail-NU/CODAH)
- DeepMind Q&A Dataset; CNN/Daily Mail (https://github.com/deepmind/rc-data)
 - Hermann et al. (2015) created two awesome datasets using news articles for Q&A research. Each dataset contains many documents (90k and 197k each), and each document companies on average 4 questions approximately. Each question is a 
sentence with one missing word/phrase which can be found from the accompanying document/context.
 - Hermann et al. (2015) created two awesome datasets using news articles for Q&A research. Each dataset contains many documents (90k and 197k each), and each document companies on average 4 questions approximately. Each question is a sentence 
with one missing word/phrase which can be found from the accompanying document/context.
 - Paper: https://arxiv.org/abs/1506.03340
- ELI5 (https://github.com/facebookresearch/ELI5)
 - Paper: https://arxiv.org/abs/1907.09190
@@ -228,22 +227,22 @@
 - A machine comprehension dataset
 - Paper: https://arxiv.org/pdf/1611.09830.pdf
- Qestion-Answer Dataset by CMU (http://www.cs.cmu.edu/~ark/QA-data/)
 - This is a corpus of Wikipedia articles, manually-generated factoid questions from them, and manually-generated answers to these questions, for use in academic research. These data were collected by Noah Smith, Michael Heilman, 
Rebecca Hwa, Shay Cohen, Kevin Gimpel, and many students at Carnegie Mellon University and the University of Pittsburgh between 2008 and 2010.
 - This is a corpus of Wikipedia articles, manually-generated factoid questions from them, and manually-generated answers to these questions, for use in academic research. These data were collected by Noah Smith, Michael Heilman, Rebecca Hwa, 
Shay Cohen, Kevin Gimpel, and many students at Carnegie Mellon University and the University of Pittsburgh between 2008 and 2010.
- SQuAD1.0 (https://rajpurkar.github.io/SQuAD-explorer/)
 - Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the
 - Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the 
corresponding reading passage, or the question might be unanswerable.
 - Paper: https://arxiv.org/abs/1606.05250
- SQuAD2.0 (https://rajpurkar.github.io/SQuAD-explorer/)
 - SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer 
questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
 - SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when 
possible, but also determine when no answer is supported by the paragraph and abstain from answering.
 - Paper: https://arxiv.org/abs/1806.03822
- Story cloze test (http://cs.rochester.edu/nlp/rocstories/)
 - 'Story Cloze Test' is a new commonsense reasoning framework for evaluating story understanding, story generation, and script learning. This test requires a system to choose the correct ending to a four-sentence story.
 - Paper: https://arxiv.org/abs/1604.01696
- TriviaQA (http://nlp.cs.washington.edu/triviaqa/)
 - TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per 
question on average, that provide high quality distant supervision for answering the questions. 
 - TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on 
average, that provide high quality distant supervision for answering the questions. 
 - Paper: https://arxiv.org/abs/1705.03551
- WikiQA (https://www.microsoft.com/en-us/download/details.aspx?id=52419&from=https%3A%2F%2Fresearch.microsoft.com%2Fen-US%2Fdownloads%2F4495da01-db8c-4041-a7f6-7984a4f6a905%2Fdefault.aspx)
 - A publicly available set of question and sentence pairs for open-domain question answering.
@@ -257,8 +256,8 @@
- 2014
 - "Problem-oriented patient record summary: An early report on a Watson application", M. Devarakonda, Dongyang Zhang, Ching-Huei Tsou, M. Bornea, Healthcom, 2014.
 - "WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information" 
(http://domino.watson.ibm.com/library/Cyberdig.nsf/1e4115aea78b6e7c85256b360066f0d4/088f74984a07645485257d5f006ace96!OpenDocument&Highlight=0,RC25489), 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.
(http://domino.watson.ibm.com/library/Cyberdig.nsf/1e4115aea78b6e7c85256b360066f0d4/088f74984a07645485257d5f006ace96!OpenDocument&Highlight=0,RC25489), 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" (http://acl2014.org/acl2014/P14-1/pdf/P14-1078.pdf), Chang Wang and James Fan, ACL, 2014.
MS Research's publication within 5 years
@@ -272,8 +271,8 @@
 - "Question-Answering with Grammatically-Interpretable Representations", Hamid Palangi, Paul Smolensky, Xiaodong He, Li Deng, 
 - "Search-based Neural Structured Learning for Sequential Question Answering", Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang, ACL, 2017.
- 2016
 - "Stacked Attention Networks for Image Question Answering" (https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yang_Stacked_Attention_Networks_CVPR_2016_paper.html), Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex 
Smola, CVPR, 2016.
 - "Stacked Attention Networks for Image Question Answering" (https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yang_Stacked_Attention_Networks_CVPR_2016_paper.html), Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, 
CVPR, 2016.
 - "Question Answering with Knowledge Base, Web and Beyond" (https://www.microsoft.com/en-us/research/publication/question-answering-with-knowledge-base-web-and-beyond/), Yih, Scott Wen-tau and Ma, Hao, ACM SIGIR, 2016.
 - "NewsQA: A Machine Comprehension Dataset" (https://arxiv.org/abs/1611.09830), Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman, RepL4NLP, 2016.
 - "Table Cell Search for Question Answering" (https://dl.acm.org/citation.cfm?id=2883080), Sun, Huan and Ma, Hao and He, Xiaodong and Yih, Wen-tau and Su, Yu and Yan, Xifeng, WWW, 2016.
@@ -282,24 +281,23 @@
 - "Web-based Question Answering: Revisiting AskMSR" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/AskMSRPlusTR_082815.pdf), Chen-Tse Tsai, Wen-tau Yih, and Christopher J.C. Burges, MSR-TR, 2015.
 - "Open Domain Question Answering via Semantic Enrichment" (https://dl.acm.org/citation.cfm?id=2741651), Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, and Ming-Wei Chang, WWW, 2015.
- 2014
 - "An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Microsoft20Deep20QA.pdf), Zhenghao Wang, Shengquan Yan, Huaming Wang, and Xuedong 
Huang, MSR-TR, 2014.
 - "An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark" (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Microsoft20Deep20QA.pdf), 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.
 
Google AI's publication within 5 years
- 2018
 - Google QA 
- **"QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension"** (https://openreview.net/pdf?id=B14TlG-RW), Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le, IC 
LR, 2018. 
- **"Ask the Right Questions: Active Question Reformulation with Reinforcement Learning"** (https://openreview.net/pdf?id=S1CChZ-CZ), Christian Buck and Jannis Bulian and Massimiliano Ciaramita and Wojciech Paweł Gajewski and Andrea Ges 
mundo and Neil Houlsby and Wei Wang, ICLR, 2018. 
- **"QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension"** (https://openreview.net/pdf?id=B14TlG-RW), 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"** (https://openreview.net/pdf?id=S1CChZ-CZ), 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"** (https://arxiv.org/pdf/1612.04342.pdf), Radu Soricut, Nan Ding, 2018. 
 - Sentence representation
- **"An efficient framework for learning sentence representations"** (https://arxiv.org/pdf/1803.02893.pdf), Lajanugen Logeswaran, Honglak Lee, ICLR, 2018.
 - "Did the model understand the question?" (https://arxiv.org/pdf/1805.05492.pdf), Pramod K. Mudrakarta and Ankur Taly and Mukund Sundararajan and Kedar Dhamdhere, ACL, 2018.
- 2017
 - "Analyzing Language Learned by an Active Question Answering Agent" (https://arxiv.org/pdf/1801.07537.pdf), Christian Buck and Jannis Bulian and Massimiliano Ciaramita and Wojciech Gajewski and Andrea Gesmundo and Neil Houlsby and 
Wei Wang, NIPS, 2017.
 - "Analyzing Language Learned by an Active Question Answering Agent" (https://arxiv.org/pdf/1801.07537.pdf), 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" (https://arxiv.org/pdf/1611.01436.pdf), Kenton Lee and Shimi Salant and Tom Kwiatkowski and Ankur Parikh and Dipanjan Das and Jonathan Berant, ICLR, 2017.
 - Identify the same question
- **"Neural Paraphrase Identification of Questions with Noisy Pretraining"** (https://arxiv.org/pdf/1704.04565.pdf), Gaurav Singh Tomar and Thyago Duque and Oscar Täckström and Jakob Uszkoreit and Dipanjan Das, SCLeM, 2017.
@@ -309,8 +307,8 @@
Facebook AI Research's publication within 5 years
- 2018
 - Embodied Question Answering (https://research.fb.com/publications/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? (https://research.fb.com/publications/do-explanations-make-vqa-models-more-predictable-to-a-human/), Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit 
Chattopadhyay, and Devi Parikh, EMNLP, 2018
 - Do explanations make VQA models more predictable to a human? (https://research.fb.com/publications/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 (https://research.fb.com/publications/neural-compositional-denotational-semantics-for-question-answering/), Nitish Gupta, Mike Lewis, EMNLP, 2018
- 2017
 - DrQA 
@@ -336,3 +334,5 @@
!CC0 (http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg) (https://creativecommons.org/share-your-work/public-domain/cc0/)
To the extent possible under law, seriousmac (https://github.com/seriousmac) (the maintainer) has waived all copyright and related or neighboring rights to this work.
qa Github: https://github.com/seriousran/awesome-qa