Conversational Feedback in Scripted versus Spontaneous Dialogues: A
  Comparative Analysis
Ildikó Pilán Laurent Prévot Hendrik Buschmeier Pierre Lison
Scripted dialogues such as movie and TV subtitles constitute a widespread
source of training data for conversational NLP models. However, the linguistic
characteristics of those dialogues are notably different from those observed in
corpora of spontaneous interactions. This difference is particularly marked for
communicative feedback and grounding phenomena such as backchannels,
acknowledgments, or clarification requests. Such signals are known to
constitute a key part of the conversation flow and are used by the dialogue
participants to provide feedback to one another on their perception of the
ongoing interaction. This paper presents a quantitative analysis of such
communicative feedback phenomena in both subtitles and spontaneous
conversations. Based on dialogue data in English, French, German, Hungarian,
Italian, Japanese, Norwegian and Chinese, we extract both lexical statistics
and classification outputs obtained with a neural dialogue act tagger. Two main
findings of this empirical study are that (1) conversational feedback is
markedly less frequent in subtitles than in spontaneous dialogues and (2)
subtitles contain a higher proportion of negative feedback. Furthermore, we
show that dialogue responses generated by large language models also follow the
same underlying trends and include comparatively few occurrences of
communicative feedback, except when those models are explicitly fine-tuned on
spontaneous dialogues.