What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text

Biaoyan Fang, Timothy Baldwin, Karin Verspoor


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.
Anthology ID:
2022.findings-acl.275
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3481–3495
Language:
URL:
https://aclanthology.org/2022.findings-acl.275
DOI:
10.18653/v1/2022.findings-acl.275
Bibkey:
Cite (ACL):
Biaoyan Fang, Timothy Baldwin, and Karin Verspoor. 2022. What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3481–3495, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text (Fang et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.275.pdf
Video:
 https://aclanthology.org/2022.findings-acl.275.mp4
Code
 biaoyanf/reciperef