Aligning Actions Across Recipe Graphs

Lucia Donatelli, Theresa Schmidt, Debanjali Biswas, Arne Köhn, Fangzhou Zhai, Alexander Koller

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

© 2021 Association for Computational LinguisticsRecipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding. One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all. Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes. We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding. We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA. We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account.
Original languageEnglish
Title of host publicationEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages6930-6942
ISBN (Electronic)9781955917094
Publication statusPublished - 2021
Externally publishedYes
Event2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
Country/TerritoryDominican Republic
CityVirtual, Punta Cana
Period7/11/2111/11/21

Funding

Partially funded by the Deutsche Forschungsge-meinschaft (DFG, German Research Foundation) – Project-ID 232722074 – SFB 1102.

FundersFunder number
Deutsche Forschungsgemeinschaft232722074 – SFB 1102

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