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Awesome Question Answering

😎 A curated list of the Question Answering (QA)

Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.

Last Update: Oct. 24, 2021, 12:08 a.m.
Thank you seriousran & contributors
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Recent Language Models

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Kevin Clark, et al., ICLR, 2020.

https://openreview.net/pdf?id=r1xMH1BtvB

TinyBERT: Distilling BERT for Natural Language Understanding

Xiaoqi Jiao, et al., ICLR, 2020.

https://openreview.net/pdf?id=rJx0Q6EFPB

MINILM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Wenhui Wang, et al., arXiv, 2020.

https://arxiv.org/abs/2002.10957

T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Colin Raffel, et al., arXiv preprint, 2019.

https://arxiv.org/abs/1910.10683

ERNIE: Enhanced Language Representation with Informative Entities

Zhengyan Zhang, et al., ACL, 2019.

https://arxiv.org/abs/1905.07129

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Zhilin Yang, et al., arXiv preprint, 2019.

https://arxiv.org/abs/1906.08237

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Zhenzhong Lan, et al., arXiv preprint, 2019.

https://arxiv.org/abs/1909.11942

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Yinhan Liu, et al., arXiv preprint, 2019.

https://arxiv.org/abs/1907.11692

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Victor sanh, et al., arXiv, 2019.

https://arxiv.org/pdf/1910.01108.pdf

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Mandar Joshi, et al., TACL, 2019.

https://arxiv.org/pdf/1907.10529v3.pdf

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin, et al., NAACL 2019, 2018.

https://arxiv.org/abs/1810.04805

AAAI 2020

TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection

Siddhant Garg, et al., AAAI 2020, Nov 2019.

https://arxiv.org/pdf/1911.04118.pdf

ACL 2019

Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

Wei Zhao, et al., ACL 2019, Jun 2019.

https://arxiv.org/pdf/1906.02829v1.pdf

Cognitive Graph for Multi-Hop Reading Comprehension at Scale

Ming Ding, et al., ACL 2019, Jun 2019.

https://arxiv.org/pdf/1905.05460v2.pdf

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

Minjoon Seo, et al., ACL 2019, Jun 2019.

https://arxiv.org/abs/1906.05807

Unsupervised Question Answering by Cloze Translation

Patrick Lewis, et al., ACL 2019, Jun 2019.

https://arxiv.org/abs/1906.04980

SemEval-2019 Task 10: Math Question Answering

Mark Hopkins, et al., ACL-W 2019, Jun 2019.

https://www.aclweb.org/anthology/S19-2153

Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

Wenhan Xiong, et al., ACL 2019, May 2019.

https://arxiv.org/abs/1905.07098

Matching Article Pairs with Graphical Decomposition and Convolutions

Bang Liu, et al., ACL 2019, May 2019.

https://arxiv.org/pdf/1802.07459v2.pdf

Episodic Memory Reader: Learning what to Remember for Question Answering from Streaming Data

Moonsu Han, et al., ACL 2019, Mar 2019.

https://arxiv.org/abs/1903.06164

Natural Questions: a Benchmark for Question Answering Research

Tom Kwiatkowski, et al., TACL 2019, Jan 2019.

https://ai.google/research/pubs/pub47761

Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension

Daesik Kim, et al., ACL 2019, Nov 2018.

https://arxiv.org/abs/1811.00232

EMNLP-IJCNLP 2019

Language Models as Knowledge Bases?

Fabio Petron, et al., EMNLP-IJCNLP 2019, Sep 2019.

https://arxiv.org/pdf/1909.01066v2.pdf

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Hao Tan, et al., EMNLP-IJCNLP 2019, Dec 2019.

https://arxiv.org/pdf/1908.07490v3.pdf

Answering Complex Open-domain Questions Through Iterative Query Generation

Peng Qi, et al., EMNLP-IJCNLP 2019, Oct 2019.

https://arxiv.org/pdf/1910.07000v1.pdf

KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning

Bill Yuchen Lin, et al., EMNLP-IJCNLP 2019, Sep 2019.

https://arxiv.org/pdf/1909.02151v1.pdf

Mixture Content Selection for Diverse Sequence Generation

Jaemin Cho, et al., EMNLP-IJCNLP 2019, Sep 2019.

https://arxiv.org/pdf/1909.01953v1.pdf

A Discrete Hard EM Approach for Weakly Supervised Question Answering

Sewon Min, et al., EMNLP-IJCNLP, 2019, Sep 2019.

https://arxiv.org/pdf/1909.04849v1.pdf

Arxiv

Investigating the Successes and Failures of BERT for Passage Re-Ranking

Harshith Padigela, et al., arXiv preprint, May 2019.

https://arxiv.org/abs/1905.01758

BERT with History Answer Embedding for Conversational Question Answering

Chen Qu, et al., arXiv preprint, May 2019.

https://arxiv.org/abs/1905.05412

Understanding the Behaviors of BERT in Ranking

Yifan Qiao, et al., arXiv preprint, Apr 2019.

https://arxiv.org/abs/1904.07531

BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis

Hu Xu, et al., arXiv preprint, Apr 2019.

https://arxiv.org/abs/1904.02232

End-to-End Open-Domain Question Answering with BERTserini

Wei Yang, et al., arXiv preprint, Feb 2019.

https://arxiv.org/abs/1902.01718

A BERT Baseline for the Natural Questions

Chris Alberti, et al., arXiv preprint, Jan 2019.

https://arxiv.org/abs/1901.08634

Passage Re-ranking with BERT

Rodrigo Nogueira, et al., arXiv preprint, Jan 2019.

https://arxiv.org/abs/1901.04085

SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering

Chenguang Zhu, et al., arXiv, Dec 2018.

https://arxiv.org/abs/1812.03593

Dataset

ELI5: Long Form Question Answering

Angela Fan, et al., ACL 2019, Jul 2019

https://arxiv.org/abs/1907.09190

Analysis and Parsing for Pre-processing in QA systems

Morphological analysis

https://www.cs.bham.ac.uk/~pjh/sem1a5/pt2/pt…

Events

Jeopardy!

champions in 2011.

https://www.jeopardy.com

Systems

IBM Watson

Has state-of-the-arts performance.

https://www.ibm.com/watson/

Facebook DrQA

Applied to the SQuAD1.0 dataset. The SQuAD2.0 dataset has released. but DrQA is not tested yet.

https://research.fb.com/downloads/drqa/

MIT media lab's Knowledge graph

Is a freely-available semantic network, designed to help computers understand the meanings of words that people use.

http://conceptnet.io/

Competitions in QA

Story Cloze Test

English | Univ. of Rochester | 2016 | msap | Logistic regression | Closed | x |

http://cs.rochester.edu/~nasrinm/files/Paper…

SQuAD

English | Univ. of Stanford | 2018 | XLNet (single model) |XLNet Team | Closed | o |

https://arxiv.org/abs/1606.05250

SQuAD1.0

https://rajpurkar.github.io/SQuAD-explorer/

TriviaQA

English | Univ. of Washington | 2017 | Ming Yan | - | Closed | - |

http://nlp.cs.washington.edu/triviaqa/

decaNLP

English | Salesforce Research | 2018 | Salesforce Research | MQAN | Closed | x |

https://decanlp.com/

DuReader Ver1.

Chinese | Baidu | 2015 | Tryer | T-Reader (single) | Closed | x |

https://ai.baidu.com/broad/introduction

KorQuAD

Korean | LG CNS AI Research | 2018 | Clova AI LaRva Team | LaRva-Kor-Large+ + CLaF (single) | Closed | o |

https://korquad.github.io/KorQuad%201.0/

KorQuAD 2.0

Korean | LG CNS AI Research | 2019 | Kangwon National University | KNU-baseline(single model) | Opened | x |

https://korquad.github.io/

CoQA

English | Univ. of Stanford | 2018 | Zhuiyi Technology | RoBERTa + AT + KD (ensemble) | Opened | o |

https://stanfordnlp.github.io/coqa/

Publications

"Learning to Skim Text"

Adams Wei Yu, Hongrae Lee, Quoc V. Le, 2017.

https://arxiv.org/pdf/1704.06877.pdf

"Deep Joint Entity Disambiguation with Local Neural Attention"

Octavian-Eugen Ganea and Thomas Hofmann, 2017.

https://arxiv.org/pdf/1704.04920.pdf

"BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION"

Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hananneh Hajishirzi, ICLR, 2017.

https://arxiv.org/pdf/1611.01603.pdf

"Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks"

Matthew Francis-Landau, Greg Durrett and Dan Klei, NAACL-HLT 2016.

http://nlp.cs.berkeley.edu/pubs/FrancisLanda…

"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.

https://ieeexplore.ieee.org/document/6823700/

"Introduction to “This is Watson"

IBM Journal of Research and Development, D. A. Ferrucci, 2012.

https://ieeexplore.ieee.org/document/6177724/

"A survey on question answering technology from an information retrieval perspective"

Information Sciences, 2011.

https://www.sciencedirect.com/science/articl…

"Question Answering in Restricted Domains: An Overview"

Diego Mollá and José Luis Vicedo, Computational Linguistics, 2007

https://www.mitpressjournals.org/doi/abs/10.…

Codes

allenai/bi-att-flow

Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization.

1.43K
683
3y 9m
Apache-2.0

NLPLearn/QANet

A Tensorflow implementation of QANet for machine reading comprehension

995
321
3y 4m
MIT

HKUST-KnowComp/R-Net

Tensorflow Implementation of R-Net

580
219
3y 78d
MIT

YerevaNN/R-NET-in-Keras

Open R-NET implementation and detailed analysis: https://git.io/vd8dx

180
93
3y 10m
MIT

hitvoice/DrQA

A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

377
98
2y 10m
n/a

google-research/bert

TensorFlow code and pre-trained models for BERT

27.01K
7.64K
1y 7m
Apache-2.0

"BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION"

Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hananneh Hajishirzi, ICLR, 2017.

https://arxiv.org/pdf/1611.01603.pdf

Paper

https://www.microsoft.com/en-us/research/wp-…

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin, et al., NAACL 2019, 2018.

https://arxiv.org/abs/1810.04805

Lectures

Question Answering - Natural Language Processing

By Dragomir Radev, Ph.D. | University of Michigan | 2016.

https://youtu.be/Kzi6tE4JaGo

Slides

scottyih/Slides

28
8
2y 80d
n/a

Question Answering

By Dr. Mariana Neves | Hasso Plattner Institut | 2017.

https://hpi.de/fileadmin/user_upload/fachgeb…

Dataset Collections

dice-group/NLIWOD

Collection of tools, utilities, datasets and approaches towards realising natural language interfaces for the Web of Data.

85
27
1y 71d
AGPL-3.0

karthikncode/nlp-datasets

A list of datasets/corpora for NLP tasks, in reverse chronological order.

904
260
4y 79d
n/a

Datasets

Websail-NU/CODAH

Repository for the CODAH dataset

16
4
1y 11d
n/a

deepmind/rc-data

Question answering dataset featured in "Teaching Machines to Read and Comprehend

1.24K
245
5y 6m
Apache-2.0

facebookresearch/ELI5

Scripts and links to recreate the ELI5 dataset.

204
26
1y 41d
n/a

ysu1989/GraphQuestions

A characteristic-rich dataset for factoid question answering described in the paper "On Generating Characteristic-rich Question Sets for QA Evaluation" - EMNLP'16

79
16
4y 7m
n/a

deepmind/narrativeqa

This repository contains the NarrativeQA dataset. It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers.

374
58
1y 6m
Apache-2.0

Maluuba/newsqa

Tools for using Maluuba's NewsQA Dataset (public version)

202
48
2y 43d
n/a

AI2 Science Questions v2.1(2017)

http://data.allenai.org/ai2-science-question…

Children's Book Test

https://uclmr.github.io/ai4exams/data.html

LC-QuAD

http://sda.cs.uni-bonn.de/projects/qa-datase…

MS MARCO

http://www.msmarco.org/dataset.aspx

MultiRC

https://cogcomp.org/multirc/

Qestion-Answer Dataset by CMU

http://www.cs.cmu.edu/~ark/QA-data/

SQuAD1.0

https://rajpurkar.github.io/SQuAD-explorer/

Story cloze test

http://cs.rochester.edu/nlp/rocstories/

TriviaQA

English | Univ. of Washington | 2017 | Ming Yan | - | Closed | - |

http://nlp.cs.washington.edu/triviaqa/

WikiQA

https://www.microsoft.com/en-us/download/det…

The DeepQA Research Team in IBM Watson's publication within 5 years

"Unsupervised Entity-Relation Analysis in IBM Watson"

Aditya Kalyanpur, J William Murdock, ACS, 2015.

http://www.cogsys.org/papers/ACS2015/article…

"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.

http://domino.watson.ibm.com/library/Cyberdi…

"Medical Relation Extraction with Manifold Models"

Chang Wang and James Fan, ACL, 2014.

http://acl2014.org/acl2014/P14-1/pdf/P14-107…

MS Research's publication within 5 years

"FigureQA: An Annotated Figure Dataset for Visual Reasoning"

Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Akos Kadar, Adam Trischler, Yoshua Bengio, ICLR, 2018

https://arxiv.org/abs/1710.07300

"Stacked Attention Networks for Image Question Answering"

Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, CVPR, 2016.

https://www.cv-foundation.org/openaccess/con…

"Question Answering with Knowledge Base, Web and Beyond"

Yih, Scott Wen-tau and Ma, Hao, ACM SIGIR, 2016.

https://www.microsoft.com/en-us/research/pub…

"NewsQA: A Machine Comprehension Dataset"

Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman, RepL4NLP, 2016.

https://arxiv.org/abs/1611.09830

"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.

https://dl.acm.org/citation.cfm?id=2883080

"WIKIQA: A Challenge Dataset for Open-Domain Question Answering"

Yi Yang, Wen-tau Yih, and Christopher Meek, EMNLP, 2015.

https://www.microsoft.com/en-us/research/wp-…

"Web-based Question Answering: Revisiting AskMSR"

Chen-Tse Tsai, Wen-tau Yih, and Christopher J.C. Burges, MSR-TR, 2015.

https://www.microsoft.com/en-us/research/wp-…

"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.

https://dl.acm.org/citation.cfm?id=2741651

"An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark"

Zhenghao Wang, Shengquan Yan, Huaming Wang, and Xuedong Huang, MSR-TR, 2014.

https://www.microsoft.com/en-us/research/wp-…

Google AI's publication within 5 years

"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.

https://openreview.net/pdf?id=B14TlG-RW

"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.

https://openreview.net/pdf?id=S1CChZ-CZ

"Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors"

Radu Soricut, Nan Ding, 2018.

https://arxiv.org/pdf/1612.04342.pdf

"An efficient framework for learning sentence representations"

Lajanugen Logeswaran, Honglak Lee, ICLR, 2018.

https://arxiv.org/pdf/1803.02893.pdf

"Did the model understand the question?"

Pramod K. Mudrakarta and Ankur Taly and Mukund Sundararajan and Kedar Dhamdhere, ACL, 2018.

https://arxiv.org/pdf/1805.05492.pdf

"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.

https://arxiv.org/pdf/1801.07537.pdf

"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.

https://arxiv.org/pdf/1611.01436.pdf

"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.

https://arxiv.org/pdf/1704.04565.pdf

Facebook AI Research's publication within 5 years

Embodied Question Answering

Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra, CVPR, 2018

https://research.fb.com/publications/embodie…

Do explanations make VQA models more predictable to a human?

Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh, EMNLP, 2018

https://research.fb.com/publications/do-expl…

Neural Compositional Denotational Semantics for Question Answering

Nitish Gupta, Mike Lewis, EMNLP, 2018

https://research.fb.com/publications/neural-…

Reading Wikipedia to Answer Open-Domain Questions

Danqi Chen, Adam Fisch, Jason Weston & Antoine Bordes, ACL, 2017.

https://cs.stanford.edu/people/danqi/papers/…

Links

Building a Question-Answering System from Scratch— Part 1

https://towardsdatascience.com/building-a-qu…

Qeustion Answering with Tensorflow By Steven Hewitt, O'REILLY, 2017

https://www.oreilly.com/ideas/question-answe…

Why question answering is hard

http://nicklothian.com/blog/2014/09/25/why-q…