Abstract
Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from materials science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A learning agent completes the game by executing the procedure correctly in a text-based simulated lab environment. The framework can complement existing approaches and enables richer forms of learning compared to static texts. We discuss potential limitations and advantages of the approach, and release a prototype proof-of-concept, hoping to encourage research in this direction.
Original language | English |
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Title of host publication | Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications |
Place of Publication | Minneapolis, Minnesota |
Publisher | Association for Computational Linguistics |
Pages | 62-71 |
Number of pages | 10 |
DOIs | |
State | Published - 1 Jun 2019 |
Event | Workshop on Extracting Structured Knowledge from Scientific Publications: NAACL HLT 2019 - Minneapolis, United States Duration: 6 Jun 2019 → … |
Conference
Conference | Workshop on Extracting Structured Knowledge from Scientific Publications |
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Country/Territory | United States |
City | Minneapolis |
Period | 6/06/19 → … |