Abstract
Multilingual code-switching research is often hindered by the lack and linguistically biased status of available datasets. To expand language representation, we synthesize code-switching data by replacing intonation units detected through PSST, a speech segmentation model fine-tuned from OpenAI’s Whisper, using a speech-to-text translation dataset, CoVoST 2. With our dataset, CoVoSwitch, spanning 13 languages, we evaluate the code-switching translation performance of two multilingual translation models, M2M-100 418M and NLLB-200 600M. We reveal that the inclusion of code-switching units results in higher translation performance than monolingual settings and that models are better at code-switching translation into English than non-English. Further, low-resource languages gain most from integration of code-switched units when translating into English but much less when translating into non-English. Translations into low-resource languages also perform worse than even raw code-switched inputs. We find that systems excel at copying English tokens but struggle with non-English tokens, that the off-target problem in monolingual settings is also relevant in code-switching settings, and that models hallucinate in code-switching translation by introducing words absent in both of the original source sentences. CoVoSwitch and code are available at https://github.com/sophiayk20/covoswitch.- Anthology ID:
- 2024.acl-srw.40
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Xiyan Fu, Eve Fleisig
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 345–357
- Language:
- URL:
- https://aclanthology.org/2024.acl-srw.40
- DOI:
- 10.18653/v1/2024.acl-srw.40
- Cite (ACL):
- Yeeun Kang. 2024. CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 345–357, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units (Kang, ACL 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.acl-srw.40.pdf