Combining Extraction and Generation for Constructing Belief-Consequence Causal Links
AffiliationDepartment of Linguistics, University of Arizona
Computer Science Department, University of Arizona
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CitationMaria Alexeeva, Allegra A. Beal Cohen, and Mihai Surdeanu. 2022. Combining Extraction and Generation for Constructing Belief-Consequence Causal Links. In Proceedings of the Third Workshop on Insights from Negative Results in NLP, pages 159–164, Dublin, Ireland. Association for Computational Linguistics.
JournalInsights 2022 - 3rd Workshop on Insights from Negative Results in NLP, Proceedings of the Workshop
RightsCopyright © 2022 Association for Computational Linguistics. This is an open access article licensed on a Creative Commons Attribution 4.0 International License.
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AbstractIn this paper, we introduce and justify a new task—causal link extraction based on beliefs—and do a qualitative analysis of the ability of a large language model—InstructGPT-3—to generate implicit consequences of beliefs. With the language model-generated consequences being promising, but not consistent, we propose directions of future work, including data collection, explicit consequence extraction using rule-based and language modeling-based approaches, and using explicitly stated consequences of beliefs to fine-tune or prompt the language model to produce outputs suitable for the task. © 2022 Association for Computational Linguistics.
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Except where otherwise noted, this item's license is described as Copyright © 2022 Association for Computational Linguistics. This is an open access article licensed on a Creative Commons Attribution 4.0 International License.