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Many natural language questions require qualitative, quantitative or logical comparisons between two entities or events.
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Xinya Du, Bhavana Dalvi, Niket Tandon, Antoine Bosselut, Wen-tau Yih, Peter Clark, and Claire Cardie. 2019 · 2019
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Dongyeop Kang, Tushar Khot, Ashish Sabharwal, and Eduard Hovy. 2018 · 2018
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Pasquale Minervini and Sebastian Riedel. 2018 · 2018
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What makes reading comprehension questions easier?
Saku Sugawara, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. 2018 · 2018
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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
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QANet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Quoc Le, Thang Luong, Rui Zhao, and Kai Chen. 2018 · 2018
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Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019a
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Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019b
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Jay Yoon Lee, Sanket Vaibhav Mehta, Michael Wick, Jean-Baptiste Tristan, and Jaime Carbonell. 2019 · 2019
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A logic-driven framework for consistency of neural models
Tao Li, Vivek Cupta, Maitrey Mehta, and Vivek Srikumar. 2019 · 2019
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QuaRel: A dataset and models for answering questions about qualitative relationships
Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, and Ashish Sabharwal. 2019 · 2019
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WIQA: A dataset for ”what if…” reasoning over procedural text
Niket Tandon, Bhavana Dalvi Mishra, Keisuke Sakaguchi, Antoine Bosselut, and Peter Clark. 2019 · 2019
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Posterior regularization for structured latent variable models
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