Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis

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Hauptverfasser: Pilán, Ildikó, Prévot, Laurent, Buschmeier, Hendrik, Lison, Pierre
Format: Preprint
Veröffentlicht: 2023
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author Pilán, Ildikó
Prévot, Laurent
Buschmeier, Hendrik
Lison, Pierre
author_facet Pilán, Ildikó
Prévot, Laurent
Buschmeier, Hendrik
Lison, Pierre
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2309_15656
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis
Pilán, Ildikó
Prévot, Laurent
Buschmeier, Hendrik
Lison, Pierre
Computation and Language
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.
title Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis
topic Computation and Language
url https://arxiv.org/abs/2309.15656