  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.
