On Evaluating Multilingual Compositional Generalization with Translated Datasets
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Compositional generalization allows efficient learning and human-like inductive biases. Since most research investigating compositional generalization in NLP is done on English, important questions remain underexplored. Do the necessary compositional generalization abilities differ across languages? Can models compositionally generalize cross-lingually? As a first step to answering these questions, recent work used neural machine translation to translate datasets for evaluating compositional generalization in semantic parsing. However, we show that this entails critical semantic distortion. To address this limitation, we craft a faithful rule-based translation of the MCWQ dataset (Cui et al., 2022) from English to Chinese and Japanese. Even with the resulting robust benchmark, which we call MCWQ-R, we show that the distribution of compositions still suffers due to linguistic divergences, and that multilingual models still struggle with cross-lingual compositional generalization. Our dataset and methodology will be useful resources for the study of cross-lingual compositional generalization in other tasks.
Originalsprog | Engelsk |
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Titel | Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
Forlag | Association for Computational Linguistics (ACL) |
Publikationsdato | 2023 |
Sider | 1669-1687 |
ISBN (Elektronisk) | 9781959429722 |
DOI | |
Status | Udgivet - 2023 |
Begivenhed | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada Varighed: 9 jul. 2023 → 14 jul. 2023 |
Konference
Konference | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 |
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Land | Canada |
By | Toronto |
Periode | 09/07/2023 → 14/07/2023 |
Sponsor | Bloomberg Engineering, et al., Google Research, Liveperson, Meta, Microsoft |
Bibliografisk note
Funding Information:
We thank the anonymous reviewers for their valuable feedback. We are also grateful to Guang Li, Nao Nakagawa, Stephanie Brandl, Ruixiang Cui, Tom Sherborne and members of the CoAStaL NLP group for their helpful insights, advice and support throughout this work.
Publisher Copyright:
© 2023 Association for Computational Linguistics.
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