论文标题
改进了大型语言模型的长期口语翻译
Improved Long-Form Spoken Language Translation with Large Language Models
论文作者
论文摘要
口语翻译中的一个挑战是，大量的口头内容是长期的，但是短单元对于获得高质量的翻译是必需的。为了解决这一不匹配，我们对通用的大语言模型进行了微调，以将长期ASR转录物分为可以独立翻译的细分市场，以最大程度地提高整体翻译质量。我们将几种细分策略进行比较，并发现我们的方法将三种语言的BLEU得分平均提高了2.7 BLEU，而自动标点基线的得分总体上。此外，我们证明了两种约束解码策略的有效性，以将模型输出从99％以上提高到100％的良好形式。
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%.
