One 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 adapt large language models (LLM) to
split long ASR transcripts into segments that can be independently translated
so as to maximize the overall translation quality. To combat the tendency of
hallucination by LLMs, we incorporate finite-state constraints during decoding
to eliminate invalid outputs. We discover that LLMs are adaptable to
transcripts containing ASR errors through prompt-tuning or fine-tuning. In
comparison to a state-of-the-art automatic punctuation baseline, our best LLM
improves the average BLEU for English-German, English-Spanish, and
English-Arabic TED talk translation in 9 test sets by 2.9 points, just by
improving segmentation.

通过将大型语言模型（LLM）用于将长 ASR 转录分割成可独立翻译的片段，以最大化整体翻译质量，采用有限状态约束进行解码以消除无效输出，通过提示调整或微调发现 LLM 可适应包含 ASR 错误的转录，与现有自动标点基线相比，我们最佳的 LLM 在 9 个测试集的英语 - 德语、英语 - 西班牙语和英语 - 阿拉伯语 TED 演讲翻译中将平均 BLEU 提高了 2.9 个点，仅通过改进分隔。