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Our goal is to better comprehend procedural text, e.g., a paragraph about photosynthesis, by not only predicting what happens, but why some actions need to happen before others.
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What happened? leveraging verbnet to predict the effects of actions in procedural text
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Ashutosh Modi. 2016 · 2016
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017a
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Peter Clark, Bhavana Dalvi Mishra, and Niket Tandon. 2018 · 2018
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Tracking state changes in procedural text: A challenge dataset and models for process paragraph comprehension
Bhavana Dalvi, Lifu Huang, Niket Tandon, Wen-tau Yih, and Peter Clark. 2018 · 2018
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Allennlp: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018 · 2018
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Learning procedures from text: Codifying how-to procedures in deep neural networks
Hogun Park and Hamid R. Motahari Nezhad. 2018 · 2018
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Reasoning about actions and state changes by injecting commonsense knowledge
Niket Tandon, Bhavana Dalvi Mishra, Joel Grus, Wen-tau Yih, Antoine Bosselut, and Peter Clark. 2018 · 2018
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Building dynamic knowledge graphs from text using machine reading comprehension
Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, and Andrew McCallum. 2019 · 2019
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