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Multi-hop question answering (QA) is a challenging task requiring QA systems to perform complex reasoning over multiple documents and provide supporting facts together with the exact answer.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016a · 2016
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Searchqa: A new q&a dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Güney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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An easy-to-hard learning paradigm for multiple classes and multiple labels
Weiwei Liu, Ivor W Tsang, and Klaus-Robert Müller. 2017 · 2017
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2017 · 2017
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Multi-step retriever-reader interaction for scalable open-domain question answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum. 2018 · 2018
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Efficient and robust question answering from minimal context over documents
Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong. 2018 · 2018
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The easy-to-hard training advantage with real-world medical images
Brett D Roads, Buyun Xu, June K Robinson, and James W Tanaka. 2018 · 2018
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The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Cited alongside, same era.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Cognitive graph for multi-hop reading comprehension at scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. 2019 · 2019
Cited alongside, same era.
Multi-hop paragraph retrieval for open-domain question answering
Yair Feldman and Ran El-Yaniv. 2019 · 2019
Cited alongside, same era.
Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs
Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, and Bowen Zhou. 2019 · 2019
Later among the works it cites.
Multi-passage BERT: A globally normalized BERT model for open-domain question answering
Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, and Bing Xiang. 2019 · 2019
Later among the works it cites.
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
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Hierarchical graph network for multi-hop question answering
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu. 2020 · 2020
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A simple yet strong pipeline for HotpotQA
Dirk Groeneveld, Tushar Khot, Mausam, and Ashish Sabharwal. 2020 · 2020
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Yichen Jiang and Mohit Bansal. 2019 · 2019
Cited alongside, same era.
Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019 · 2019
Cited alongside, same era.
Answering while summarizing: Multi-task learning for multi-hop QA with evidence extraction
Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, and Junji Tomita. 2019 · 2019
Cited alongside, same era.
Answering complex open-domain questions through iterative query generation
Peng Qi, Xiaowen Lin, Leo Mehr, Zijian Wang, and Christopher D. Manning. 2019 · 2019
Cited alongside, same era.
Dynamically fused graph network for multi-hop reasoning
Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019 · 2019
Cited alongside, same era.
An easy-to-hard learning strategy for within-image co-saliency detection
Shaoyue Song, Hongkai Yu, Zhenjiang Miao, Dazhou Guo, Wei Ke, Cong Ma, and Song Wang. 2019 · 2019
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016b
Cited in the paper.
Unsupervised question decomposition for question answering
Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela. 2020 · 2020
Later among the works it cites.
Is Graph Structure Necessary for Multi-hop Question Answering?
Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, and Guoping Hu. 2020 · 2020
Later among the works it cites.
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Ming Tu, Bowen Zhou, et al. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Alexander M. Rush, et al. 2020 · 2020
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Asynchronous multi-grained graph network for interpretable multi-hop reading comprehension
Ronghan Li, Lifang Wang, Shengli Wang, and Zejun Jiang. 2021 · 2021
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Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy
Bohong Wu, Zhuosheng Zhang, and Hai Zhao. 2021 · 2021
Later among the works it cites.