Towards Flow Graph Prediction of Open-Domain Procedural Texts

Keisuke Shirai, Hirotaka Kameko, Shinsuke Mori

Abstract
Machine comprehension of procedural texts is essential for reasoning about the steps and automating the procedures. However, this requires identifying entities within a text and resolving the relationships between the entities. Previous work focused on the cooking domain and proposed a framework to convert a recipe text into a flow graph (FG) representation. In this work, we propose a framework based on the recipe FG for flow graph prediction of open-domain procedural texts. To investigate flow graph prediction performance in non-cooking domains, we introduce the wikiHow-FG corpus from articles on wikiHow, a website of how-to instruction articles. In experiments, we consider using the existing recipe corpus and performing domain adaptation from the cooking to the target domain. Experimental results show that the domain adaptation models achieve higher performance than those trained only on the cooking or target domain data.
Anthology ID:
2023.repl4nlp-1.8
Volume:
Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Burcu Can, Maximilian Mozes, Samuel Cahyawijaya, Naomi Saphra, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Chen Zhao, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Lena Voita
Venue:
RepL4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
87–96
Language:
URL:
https://aclanthology.org/2023.repl4nlp-1.8
DOI:
10.18653/v1/2023.repl4nlp-1.8
Bibkey:
Cite (ACL):
Keisuke Shirai, Hirotaka Kameko, and Shinsuke Mori. 2023. Towards Flow Graph Prediction of Open-Domain Procedural Texts. In Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023), pages 87–96, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Towards Flow Graph Prediction of Open-Domain Procedural Texts (Shirai et al., RepL4NLP 2023)
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https://preview.aclanthology.org/landing_page/2023.repl4nlp-1.8.pdf
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