awesome-qa
by seriousran
😎 A curated list of the Question Answering (QA)
AI summary
NLP QA
A curated list of question answering systems and techniques in natural language processing
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What's in the list
143 links in 66 sections, with live GitHub stats.activeno commit in 2y
Recent Trends / Recent QA Models / DilBert: Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering (2020)
https://github.com/wissam-sib/dilbertRecent Trends / Recent QA Models / UnifiedQA: Crossing Format Boundaries With a Single QA System (2020)
Recent Trends / Recent QA Models / ProQA: Resource-efficient method for pretraining a dense corpus index for open-domain QA and IR. (2020)
https://github.com/xwhan/ProQARecent Trends / Recent QA Models / TYDI QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020)
Recent Trends / Recent QA Models / Retrospective Reader for Machine Reading Comprehension
Recent Trends / Recent QA Models / TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection (AAAI 2020)
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
Recent Trends / AAAI 2020
- TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
, Siddhant Garg, et al., AAAI 2020, Nov 2019
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
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
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
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
About QA / Analysis and Parsing for Pre-processing in QA systems
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
Competitions in QA
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
Publications / Papers / "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks"
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
Codes
BiDAF
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
Codes / BiDAF
Codes
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
R-Net
An end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage
Codes / R-Net
Codes
R-Net-in-Keras
R-NET re-implementation in Keras
Codes / R-Net-in-Keras
Codes
DrQA
DrQA is a system for reading comprehension applied to open-domain question answering
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
Codes / BERT
Lectures
- Question Answering - Natural Language Processing
By Dragomir Radev, Ph.D. | University of Michigan | 2016
Slides
Question Answering with Knowledge Bases, Web and Beyond
By Scott Wen-tau Yih & Hao Ma | Microsoft Research | 2016
- Question Answering
By Dr. Mariana Neves | Hasso Plattner Institut | 2017
Dataset Collections
NLIWOD's Question answering datasets
karthinkncode's Datasets for Natural Language ProcessingDatasets
Datasets / AI2 Science Questions v2.1(2017)
Datasets
CODAH Dataset
DeepMind Q&A Dataset; CNN/Daily MailDatasets / DeepMind Q&A Dataset; CNN/Daily Mail
Datasets
ELI5Datasets / ELI5
Datasets
GraphQuestionsDatasets / MS MARCO
Datasets
Datasets / MultiRC
Datasets
Datasets / NarrativeQA
Datasets
NewsQADatasets / NewsQA
Datasets
Datasets / SQuAD1.0
Datasets
Datasets / SQuAD2.0
Datasets
Datasets / Story cloze test
Datasets
Datasets / TriviaQA
Datasets
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
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
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
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
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
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
Datasets / Google AI's publication within 5 years / 2018
- "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
Datasets / Google AI's publication within 5 years / 2018 / Sentence representation
- "An efficient framework for learning sentence representations"
, Lajanugen Logeswaran, Honglak Lee, ICLR, 2018
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
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
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
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
Datasets / Facebook AI Research's publication within 5 years / 2017
- Reading Wikipedia to Answer Open-Domain Questions
, Danqi Chen, Adam Fisch, Jason Weston & Antoine Bordes, ACL, 2017
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