Document Type

Conference Proceeding

Publication Title

Proceedings of the Annual Meeting of the Association for Computational Linguistics

Abstract

Procedural text contains rich anaphoric phenomena, yet has not received much attention in NLP. To fill this gap, we investigate the textual properties of two types of procedural text, recipes and chemical patents, and generalize an anaphora annotation framework developed for the chemical domain for modeling anaphoric phenomena in recipes. We apply this framework to annotate the RecipeRef corpus with both bridging and coreference relations. Through comparison to chemical patents, we show the complexity of anaphora resolution in recipes. We demonstrate empirically that transfer learning from the chemical domain improves resolution of anaphora in recipes, suggesting transferability of general procedural knowledge.

First Page

3481

Last Page

3495

DOI

10.18653/v1/2022.findings-acl.275

Publication Date

5-22-2022

Comments

Open Access version available on ACL

Archived with thanks to ACL

License: CC by 4.0

Uploaded 16 November 2023

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