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State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke S. Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Hierarchical graph network for multi-hop question answering
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jing jing Liu. 2019 · 1911
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Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents
Ming Tu, Kevin Huang, Guangtao Wang, Jui-Ting Huang, Xiaodong He, and Bufang Zhou. 2019 · 1911
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
Cited alongside, same era.
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 W. 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.
Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke S. Zettlemoyer. 2019a
Cited in the paper.
Revealing the importance of semantic retrieval for machine reading at scale
Yixin Nie, Songhe Wang, and Mohit Bansal. 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
Dynamically fused graph network for multi-hop reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019 · 2019
Later among the works it cites.
QASC: A dataset for question answering via sentence composition
Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020 · 2020
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Sewon Min, Victor Zhong, Luke S. Zettlemoyer, and Hannaneh Hajishirzi. 2019b
Cited in the paper.