https://en.wikipedia.org/wiki/Question_answering,Question Answering (QA),https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1910.10683,T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1901.08634,A BERT Baseline for the Natural Questions,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yang_Stacked_Attention_Networks_CVPR_2016_paper.html,"Stacked Attention Networks for Image Question Answering",https://github.com/seriousran/awesome-qa#readme,Question Answering
http://data.allenai.org/ai2-science-questions/,AI2 Science Questions v2.1(2017),https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1905.05460v2.pdf,Cognitive Graph for Multi-Hop Reading Comprehension at Scale,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1704.04920.pdf,"Deep Joint Entity Disambiguation with Local Neural Attention",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1908.07490v3.pdf,LXMERT: Learning Cross-Modality Encoder Representations from Transformers,https://github.com/seriousran/awesome-qa#readme,Question Answering
http://conceptnet.io/,MIT media lab's Knowledge graph,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1612.04342.pdf,"Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/2010.08422.pdf,https://arxiv.org/pdf/2010.08422.pdf,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1905.07129,ERNIE: Enhanced Language Representation with Informative Entities,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1901.04085,Passage Re-ranking with BERT,https://github.com/seriousran/awesome-qa#readme,Question Answering
http://ai2-website.s3.amazonaws.com/publications/AI2ReasoningChallenge2018.pdf,http://ai2-website.s3.amazonaws.com/publications/AI2ReasoningChallenge2018.pdf,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1906.05807,Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1812.03593,SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1906.08237,XLNet: Generalized Autoregressive Pretraining for Language Understanding,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1611.01603.pdf,"BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1910.07000v1.pdf,Answering Complex Open-domain Questions Through Iterative Query Generation,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://unifiedqa.apps.allenai.org/,https://unifiedqa.apps.allenai.org/,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1803.02893.pdf,"An efficient framework for learning sentence representations",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://towardsdatascience.com/building-a-question-answering-system-part-1-9388aadff507,Building a Question-Answering System from Scratch— Part 1,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.microsoft.com/en-us/research/publication/question-answering-with-knowledge-base-web-and-beyond/,"Question Answering with Knowledge Base, Web and Beyond",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1906.04980,Unsupervised Question Answering by Cloze Translation,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1909.11942,ALBERT: A Lite BERT for Self-supervised Learning of Language Representations,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1907.09190,ELI5: Long Form Question Answering,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1611.09830,"NewsQA: A Machine Comprehension Dataset",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1909.02151v1.pdf,KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/2005.00038.pdf,https://arxiv.org/pdf/2005.00038.pdf,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1805.05492.pdf,"Did the model understand the question?",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1907.11692,RoBERTa: A Robustly Optimized BERT Pretraining Approach,https://github.com/seriousran/awesome-qa#readme,Question Answering
http://nlp.cs.berkeley.edu/pubs/FrancisLandau-Durrett-Klein_2016_EntityConvnets_paper.pdf,"Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks",https://github.com/seriousran/awesome-qa#readme,Question Answering
http://www.cs.cmu.edu/~ark/QA-data/,Qestion-Answer Dataset by CMU,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1606.05250,SQuAD,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/ftp/arxiv/papers/2003/2003.05002.pdf,https://arxiv.org/ftp/arxiv/papers/2003/2003.05002.pdf,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.oreilly.com/ideas/question-answering-with-tensorflow,Qeustion Answering with Tensorflow By Steven Hewitt, O'REILLY, 2017,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://dl.acm.org/citation.cfm?id=2883080,"Table Cell Search for Question Answering",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1909.01953v1.pdf,Mixture Content Selection for Diverse Sequence Generation,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1806.03822,https://arxiv.org/abs/1806.03822,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1801.07537.pdf,"Analyzing Language Learned by an Active Question Answering Agent",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1910.01108.pdf,DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://ieeexplore.ieee.org/document/6823700/,"Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.aclweb.org/anthology/S19-2153,SemEval-2019 Task 10: Math Question Answering,https://github.com/seriousran/awesome-qa#readme,Question Answering
http://nlp.cs.washington.edu/triviaqa/,TriviaQA,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/2001.09694v2.pdf,https://arxiv.org/pdf/2001.09694v2.pdf,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://decanlp.com/,decaNLP,https://github.com/seriousran/awesome-qa#readme,Question Answering
http://cs.rochester.edu/nlp/rocstories/,Story cloze test,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1909.04849v1.pdf,A Discrete Hard EM Approach for Weakly Supervised Question Answering,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1905.07098,Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://ieeexplore.ieee.org/document/6177724/,"Introduction to “This is Watson",https://github.com/seriousran/awesome-qa#readme,Question Answering
http://nicklothian.com/blog/2014/09/25/why-question-answering-is-hard/,Why question answering is hard,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1611.01436.pdf,"Learning Recurrent Span Representations for Extractive Question Answering",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1604.01696,https://arxiv.org/abs/1604.01696,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1911.04118.pdf,https://arxiv.org/pdf/1911.04118.pdf,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1905.01758,Investigating the Successes and Failures of BERT for Passage Re-Ranking,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1802.07459v2.pdf,Matching Article Pairs with Graphical Decomposition and Convolutions,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1704.04565.pdf,"Neural Paraphrase Identification of Questions with Noisy Pretraining",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1705.03551,https://arxiv.org/abs/1705.03551,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1905.05412,BERT with History Answer Embedding for Conversational Question Answering,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/YangYihMeek_EMNLP-15_WikiQA.pdf,"WIKIQA: A Challenge Dataset for Open-Domain Question Answering",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://openreview.net/pdf?id=r1xMH1BtvB,ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1903.06164,Episodic Memory Reader: Learning what to Remember for Question Answering from Streaming Data,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://research.fb.com/publications/embodied-question-answering/,Embodied Question Answering,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1904.07531,Understanding the Behaviors of BERT in Ranking,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://ai.baidu.com/broad/introduction,DuReader Ver1.,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/AskMSRPlusTR_082815.pdf,"Web-based Question Answering: Revisiting AskMSR",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://openreview.net/pdf?id=rJx0Q6EFPB,TinyBERT: Distilling BERT for Natural Language Understanding,https://github.com/seriousran/awesome-qa#readme,Question Answering
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,WikiQA,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.sciencedirect.com/science/article/pii/S0020025511003860,"A survey on question answering technology from an information retrieval perspective",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://research.fb.com/publications/do-explanations-make-vqa-models-more-predictable-to-a-human/,Do explanations make VQA models more predictable to a human?,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://korquad.github.io/KorQuad%201.0/,KorQuAD,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1907.10529v3.pdf,SpanBERT: Improving Pre-training by Representing and Predicting Spans,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://ai.google/research/pubs/pub47761,Natural Questions: a Benchmark for Question Answering Research,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://korquad.github.io/,KorQuAD 2.0,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1904.02232,BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://research.fb.com/publications/neural-compositional-denotational-semantics-for-question-answering/,Neural Compositional Denotational Semantics for Question Answering,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/2002.10957,MINILM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://stanfordnlp.github.io/coqa/,CoQA,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1811.00232,Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1902.01718,End-to-End Open-Domain Question Answering with BERTserini,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://dl.acm.org/citation.cfm?id=2741651,"Open Domain Question Answering via Semantic Enrichment",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1704.06877.pdf,"Learning to Skim Text",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1909.01066v2.pdf,Language Models as Knowledge Bases?,https://github.com/seriousran/awesome-qa#readme,Question Answering
http://acl2014.org/acl2014/P14-1/pdf/P14-1078.pdf,"Medical Relation Extraction with Manifold Models",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Microsoft20Deep20QA.pdf,"An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.mitpressjournals.org/doi/abs/10.1162/coli.2007.33.1.41,"Question Answering in Restricted Domains: An Overview",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://openreview.net/pdf?id=B14TlG-RW,"QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1710.07300,"FigureQA: An Annotated Figure Dataset for Visual Reasoning",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf,Paper,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://openreview.net/pdf?id=S1CChZ-CZ,"Ask the Right Questions: Active Question Reformulation with Reinforcement Learning",https://github.com/seriousran/awesome-qa#readme,Question Answering
https://youtu.be/Kzi6tE4JaGo,Question Answering - Natural Language Processing,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://hpi.de/fileadmin/user_upload/fachgebiete/plattner/teaching/NaturalLanguageProcessing/NLP2017/NLP8_QuestionAnswering.pdf,Question Answering,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1506.03340,https://arxiv.org/abs/1506.03340,https://github.com/seriousran/awesome-qa#readme,Question Answering
http://sda.cs.uni-bonn.de/projects/qa-dataset/,LC-QuAD,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/abs/1611.09268,https://arxiv.org/abs/1611.09268,https://github.com/seriousran/awesome-qa#readme,Question Answering
http://cogcomp.org/page/publication_view/833,http://cogcomp.org/page/publication_view/833,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1712.07040v1.pdf,https://arxiv.org/pdf/1712.07040v1.pdf,https://github.com/seriousran/awesome-qa#readme,Question Answering
https://arxiv.org/pdf/1611.09830.pdf,https://arxiv.org/pdf/1611.09830.pdf,https://github.com/seriousran/awesome-qa#readme,Question Answering