Abstract
While multilingual language models can improve NLP performance on low-resource languages by leveraging higher-resource languages, they also reduce average performance on all languages (the ‘curse of multilinguality’). Here we show another problem with multilingual models: grammatical structures in higher-resource languages bleed into lower-resource languages, a phenomenon we call grammatical structure bias. We show this bias via a novel method for comparing the fluency of multilingual models to the fluency of monolingual Spanish and Greek models: testing their preference for two carefully-chosen variable grammatical structures (optional pronoun-drop in Spanish and optional Subject-Verb ordering in Greek). We find that multilingual BERT is biased toward the English-like setting (explicit pronouns and Subject-Verb-Object ordering) as compared to our monolingual control language model. With our case studies, we hope to bring to light the fine-grained ways in which multilingual models can be biased, and encourage more linguistically-aware fluency evaluation.- Anthology ID:
- 2023.findings-eacl.89
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2023
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1194–1200
- Language:
- URL:
- https://aclanthology.org/2023.findings-eacl.89
- DOI:
- 10.18653/v1/2023.findings-eacl.89
- Cite (ACL):
- Isabel Papadimitriou, Kezia Lopez, and Dan Jurafsky. 2023. Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1194–1200, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models (Papadimitriou et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2023.findings-eacl.89.pdf