  A challenge in spoken language translation is that plenty of spoken content
is long-form, but short units are necessary for obtaining high-quality
translations. To address this mismatch, we fine-tune a general-purpose, large
language model to split long ASR transcripts into segments that can be
independently translated so as to maximize the overall translation quality. We
compare to several segmentation strategies and find that our approach improves
BLEU score on three languages by an average of 2.7 BLEU overall compared to an
automatic punctuation baseline. Further, we demonstrate the effectiveness of
two constrained decoding strategies to improve well-formedness of the model
output from above 99% to 100%.
