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Speech and Natural Language Processing > Question Answering

The science of asking and answering in natural language with a machine.

Collection 767 stars GitHub

Contents

Recent Trends
Codes
Publications
Slides
Dataset Collections
Datasets
License

Recent Trends

Recent QA Models

DilBert: Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering (2020) 16 updated 5y ago

paper: https://arxiv.org/pdf/2010.08422.pdf

ProQA: Resource-efficient method for pretraining a dense corpus index for open-domain QA and IR. (2020) 43 updated 2y ago

paper: https://arxiv.org/pdf/2005.00038.pdf

UnifiedQA: Crossing Format Boundaries With a Single QA System (2020) 445 updated 4y ago

Demo: https://unifiedqa.apps.allenai.org/

TYDI QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020)
Retrospective Reader for Machine Reading Comprehension

AAAI 2020

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

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

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

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

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 6.2k updated 2y ago

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

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations 3.3k (archived)

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.

SpanBERT: Improving Pre-training by Representing and Predicting Spans 906 (archived)

Mandar Joshi, et al., TACL, 2019.

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Zhilin Yang, 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 160.1k updated 5d ago

Victor sanh, et al., arXiv, 2019.

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

Kevin Clark, et al., ICLR, 2020.

ERNIE: Enhanced Language Representation with Informative Entities

Zhengyan Zhang, et al., ACL, 2019.

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

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

TinyBERT: Distilling BERT for Natural Language Understanding

Xiaoqi Jiao, et al., ICLR, 2020.

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Mandar Joshi, et al., TACL, 2019.

ACL 2019

SemEval-2019 Task 10: Math Question Answering

, Mark Hopkins, et al., ACL-W 2019, Jun 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 458 updated 3y ago

, 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 218 (archived)

, Patrick Lewis, et al., ACL 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 1.1k (archived)

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.

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 1.8k updated 15d ago

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.

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.

Cognitive Graph for Multi-Hop Reading Comprehension at Scale

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

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

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

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

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

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.

SemEval-2019 Task 10: Math Question Answering

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

Codes

BERT 39.9k (archived)

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.

BiDAF 1.5k updated 2y ago

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.

QANet 982 updated 8y ago

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 577 updated 7y ago

An end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage.

R-Net-in-Keras 177 updated 8y ago

R-NET re-implementation in Keras.

DrQA 403 updated 4y ago

DrQA is a system for reading comprehension applied to open-domain question answering.

Publications

Papers

Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks 58 updated 9y ago

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

Slides

Dataset Collections

Datasets

CODAH Dataset 22 updated 3y ago
DeepMind Q&A Dataset; CNN/Daily Mail 1.3k (archived)

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.

ELI5 325 (archived)

Paper: https://arxiv.org/abs/1907.09190

GraphQuestions 93 updated 3y ago

On generating Characteristic-rich Question sets for QA evaluation.

NarrativeQA 508 updated 6y ago

It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers.

NewsQA 256 (archived)

A machine comprehension dataset

Neural Compositional Denotational Semantics for Question Answering

Nitish Gupta, Mike Lewis, EMNLP, 2018

Reading Wikipedia to Answer Open-Domain Questions

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

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

NewsQA: A Machine Comprehension Dataset

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

WIKIQA: A Challenge Dataset for Open-Domain Question Answering

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

FigureQA: An Annotated Figure Dataset for Visual Reasoning

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

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.

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.

An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark

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

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

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

An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark

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

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

Web-based Question Answering: Revisiting AskMSR

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

An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark

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

License