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Multi-hop QA requires a model to connect multiple pieces of evidence scattered in a long context to answer the question.
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Bidirectional attention flow for machine comprehension
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Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2017 · 2017
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Transparency by design: Closing the gap between performance and interpretability in visual reasoning
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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
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Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2017 · 2017
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Using syntax to ground referring expressions in natural images
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Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner. 2018 · 2018
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Explainable neural computation via stack neural module networks
Ronghang Hu, Jacob Andreas, Trevor Darrell, and Kate Saenko. 2018 · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2018 · 2018
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Learning to compose neural networks for question answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016a
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016b
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Understanding dataset design choices for multi-hop reasoning
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Avoiding reasoning shortcuts: Adversarial evaluation, training, and model development for multi-hop qa
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Coarse-grain fine-grain coattention network for multi-evidence question answering
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