Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis
This addresses the issue of training data bias for conversational NLP models, but it is incremental as it quantifies known differences without proposing a new solution.
The paper tackled the problem of linguistic differences in communicative feedback between scripted dialogues (e.g., subtitles) and spontaneous conversations, finding that feedback is less frequent and more negative in subtitles, and that LLM-generated dialogues resemble scripted ones more closely.
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.