- [2013 EMNLP] MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text, [paper], [homepage], source: [mcobzarenco/mctest].
- [2015 NIPS] CNN/DailyMail: Teaching Machines to Read and Comprehend, [paper], [homepage], sources: [thomasmesnard/DeepMind-Teaching-Machines-to-Read-and-Comprehend].
- [2016 EMNLP] SQuAD 100,000+ Questions for Machine Comprehension of Text, [paper], [homepage].
- [2016 ICLR] bAbI: Towards AI-Complete Question Answering: a Set of Prerequisite Toy Tasks, [paper], [homepage], sources: [facebook/bAbI-tasks].
- [2017 EMNLP] World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions, [paper], [homepage].
- [2017 EMNLP] RACE: Large-scale ReAding Comprehension Dataset From Examinations, [paper], [homepage], sources: [qizhex/RACE_AR_baselines].
- [2017 ACL] TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension, [paper], [homepage], sources: [mandarjoshi90/triviaqa].
- [2017 ArXiv] QAngaroo: Constructing Datasets for Multi-hop Reading Comprehension Across Documents, [paper], [homepage],
- [2018 ICLR] CLOTH: Large-scale Cloze Test Dataset Designed by Teachers, [paper], [homepage], sources: [qizhex/Large-scale-Cloze-Test-Dataset-Designed-by-Teachers].
- [2018 NAACL] MultiRC: Looking Beyond the Surface -- A Challenge Set for Reading Comprehension over Multiple Sentences, [paper], [homepage], sources: [CogComp/multirc].
- [2014 NIPS] Deep Learning for Answer Sentence Selection, [paper], sources: [brmson/Sentence-selection].
- [2015 NIPS] Pointer Networks, [paper], [blog], sources: [devsisters/pointer-network-tensorflow], [https://github.com/ikostrikov/TensorFlow-Pointer-Networks], [keon/pointer-networks], [pemami4911/neural-combinatorial-rl-pytorch], [shiretzet/PointerNet].
- [2016 ICLR] LSTM-based Deep Learning Models for Non-factoid Answer Selection, [paper], sources: [Alan-Lee123/answer-selection], [tambetm/allenAI].
- [2016 ACL] A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task, [paper], sources: [danqi/rc-cnn-dailymail].
- [2017 ICLR] Query-Reduction Networks for Question Answering, [paper], [homepage], sources: [uwnlp/qrn].
- [2017 ICLR] Bi-Directional Attention Flow for Machine Comprehension, [paper], [homepage], [demo], sources: [allenai/bi-att-flow].
- [2017 ACL] R-Net: Machine Reading Comprehension with Self-matching Networks, [paper], [blog], sources: [HKUST-KnowComp/R-Net], [YerevaNN/R-NET-in-Keras], [minsangkim142/R-net].
- [2017 ArXiv] Simple and Effective Multi-Paragraph Reading Comprehension, [paper], sources: [allenai/document-qa].
- [2017 CoNLL] Making Neural QA as Simple as Possible but not Simpler, [paper], [homepage], [github-page], sources: [georgwiese/biomedical-qa].
- [2017 EMNLP] Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension, [paper], sources: [davidgolub/QuestionGeneration].
- [2017 ACL] Attention-over-Attention Neural Networks for Reading Comprehension, [paper], sources: [OlavHN/attention-over-attention], [marshmelloX/attention-over-attention].
- [2018 ICLR] MaskGAN: Better Text Generation via Filling in the
______
, [paper]. - [2018 AAAI] Multi-attention Recurrent Network for Human Communication Comprehension, [paper].
- [2018 ICLR] FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension, [paper], sources: [exe1023/FusionNet], [momohuang/FusionNet-NLI].
- [2018 NAACL] Contextualized Word Representations for Reading Comprehension, [paper], sources: [shimisalant/CWR].
- [2018 ICLR] QANet: Combing Local Convolution with Global Self-Attention for Reading Comprehension, [paper], sources: [hengruo/QANet-pytorch], [NLPLearn/QANet].
- [2018 ACL] Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge, [paper].
- [2014 ACL] Freebase QA: Information Extraction or Semantic Parsing?, [paper].
- [2016 ACL] Question Answering on Freebase via Relation Extraction and Textual Evidence, [paper], sources: [syxu828/QuestionAnsweringOverFB].
- [2017 ACL] An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge, [paper], [homepage], [blog].
- [2017 ACL] Improved Neural Relation Detection for Knowledge Base Question Answering, [paper].
- [2017 ACL] Reading Wikipedia to Answer Open-Domain Questions, [paper], sources: [facebookresearch/DrQA], [hitvoice/DrQA].
- [2017 ArXiv] Dynamic Integration of Background Knowledge in Neural NLU Systems, [paper], [homepage].
- [2018 ArXiv] An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering, [paper].
- [2018 SemEval] Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension, [paper], sources: [intfloat/commonsense-rc].
- [2015 ICLR] Memory Networks, [paper], sources: [facebook/MemNN].
- [2015 NIPS] End-To-End Memory Networks, [paper], sources: [facebook/MemNN], [seominjoon/memnn-tensorflow], [domluna/memn2n], [carpedm20/MemN2N-tensorflow].
- [2016 ICML] Dynamic Memory Networks for Visual and Textual Question Answering, [paper], [blog], sources: [therne/dmn-tensorflow], [barronalex/Dynamic-Memory-Networks-in-TensorFlow], [ethancaballero/Improved-Dynamic-Memory-Networks-DMN-plus], [dandelin/Dynamic-memory-networks-plus-Pytorch], [DeepRNN/visual_question_answering].
- [2016 ICML] Ask Me Anything: Dynamic Memory Networks for Natural Language Processing, [paper], sources: [DongjunLee/dmn-tensorflow].
- [2016 EMNLP] Long Short-Term Memory-Networks for Machine Reading, [paper], sources: [cheng6076/SNLI-attention], [vsitzmann/snli-attention-tensorflow].
- [2017 ACL] Learning to Skim Text, [paper], [notes].
- [2017 ICLR] Variable Computation in Recurrent Neural Networks, [paper].
- [2017 ICLR] Machine Comprehension Using Match-LSTM and Answer Pointer, [paper], sources: [shuohangwang/SeqMatchSeq], [MurtyShikhar/Question-Answering], [InnerPeace-Wu/reading_comprehension-cs224n].
- [2018 ICLR] Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks, [paper], [homepage], sources: [imatge-upc/skiprnn-2017-telecombcn].
- [2018 ICLR] Neural Speed Reading via Skim-RNN, [paper], sources: [schelotto/Neural_Speed_Reading_via_Skim-RNN_PyTorch].