脚本对话与自发对话中的会话反馈：一项比较分析
Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis
AI总结：
本文通过八种语言对话数据比较脚本字幕与自发对话中的交际反馈，发现字幕反馈更少且负面反馈更多，并指出大语言模型生成的对话在反馈特征上更接近脚本对话。
摘要：
电影和电视字幕等脚本对话构成了对话式自然语言处理模型广泛使用的训练数据来源。然而，这些对话与自发互动之间存在显著的语言学差异，尤其是在交际反馈（如反向通道、确认回应或澄清请求）的出现方面。本文对字幕和自发对话中的此类反馈现象进行了定量分析。基于涵盖八种语言和多种体裁的对话数据，我们提取了词汇统计信息、来自对话行为标注器的分类、专家标注以及由微调后的大语言模型（LLM）推导出的标签。我们的主要实证发现是：（1）交际反馈在字幕中的出现频率明显低于自发对话；（2）字幕中包含更高比例的负面反馈。我们还表明，标准大语言模型生成的对话在交际反馈方面更接近脚本对话，而非自发互动。
英文摘要：
Scripted dialogues such as movie and TV subtitles constitute a widespread source of training data for conversational NLP models. However, there are notable linguistic differences between these dialogues and spontaneous interactions, especially regarding the occurrence of communicative feedback such as backchannels, acknowledgments, or clarification requests. This paper presents a quantitative analysis of such feedback phenomena in both subtitles and spontaneous conversations. Based on conversational data spanning eight languages and multiple genres, we extract lexical statistics, classifications from a dialogue act tagger, expert annotations and labels derived from a fine-tuned Large Language Model (LLM). Our main empirical findings are that (1) communicative feedback is markedly less frequent in subtitles than in spontaneous dialogues and (2) subtitles contain a higher proportion of negative feedback. We also show that dialogues generated by standard LLMs lie much closer to scripted dialogues than spontaneous interactions in terms of communicative feedback.
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。
