CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units

• URL: http://arxiv.org/abs/2407.14295v1
• Date: Fri, 19 Jul 2024 13:26:35 GMT
• Title: CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units
• Authors: Yeeun Kang,
• Abstract summary: We synthesize code-switching data by replacing intonation units detected through PSST. We evaluate the code-switching translation performance of two multilingual translation models, M2M-100 418M and NLLB-200 600M.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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