基于大语言模型的跨语言对话语音摘要生成
Cross-Lingual Conversational Speech Summarization with Large Language Models
AI总结：
针对跨语言对话语音摘要资源匮乏问题，研究者在Fisher和Callhome西英语音翻译语料库基础上补充GPT-4生成的摘要构建数据集，适配后的Mistral-7B模型摘要性能优于现成模型且媲美GPT-4。
摘要：
跨语言对话语音摘要是一个重要问题，但面临资源匮乏的困境。尽管已有多种语言的转录文本，但翻译后的对话语音十分稀缺，且不存在包含摘要的数据集。我们在现有的Fisher和Callhome西班牙语-英语语音翻译语料库基础上，为译文补充了摘要。这些摘要由GPT-4基于参考译文生成，并被作为基准真值。该任务的目标是在存在转录和翻译错误的情况下生成类似的摘要。我们使用开源语音识别与机器翻译模型构建了一个基于级联的基线系统。我们测试了多种用于摘要生成的大语言模型，并分析了转录和翻译错误的影响。针对该任务适配的Mistral-7B模型表现显著优于现成模型，且性能与GPT-4相当。
英文摘要：
Cross-lingual conversational speech summarization is an important problem, but suffers from a dearth of resources. While transcriptions exist for a number of languages, translated conversational speech is rare and datasets containing summaries are non-existent. We build upon the existing Fisher and Callhome Spanish-English Speech Translation corpus by supplementing the translations with summaries. The summaries are generated using GPT-4 from the reference translations and are treated as ground truth. The task is to generate similar summaries in the presence of transcription and translation errors. We build a baseline cascade-based system using open-source speech recognition and machine translation models. We test a range of LLMs for summarization and analyze the impact of transcription and translation errors. Adapting the Mistral-7B model for this task performs significantly better than off-the-shelf models and matches the performance of GPT-4.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
