# Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis

arXiv: https://arxiv.org/abs/2309.15656 · HTML: https://mlsanity.com/paper/2309.15656/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 96 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ](https://mlsanity.com/paper/1703.00565/) (2017)
- [SocialDial: A Benchmark for Socially-Aware Dialogue Systems](https://mlsanity.com/paper/2304.12026/) (2023)
- [Should we Stop Training More Monolingual Models, and Simply Use Machine Translation Instead?](https://mlsanity.com/paper/2104.10441/) (2021)
- [Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented Dialogues and Annotations](https://mlsanity.com/paper/2305.14556/) (2023)
- [ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents](https://mlsanity.com/paper/1806.04327/) (2018)

## Built on by (most cited first, 10 of 10)

- [Grounding Gaps in Language Model Generations](https://mlsanity.com/paper/2311.09144/) (2023)
- [Large Language Models Know What To Say But Not When To Speak](https://mlsanity.com/paper/2410.16044/) (2024)
- [Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions](https://mlsanity.com/paper/2506.08952/) (2025)
- [Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches](https://mlsanity.com/paper/2404.14465/) (2024)
- [Classification of Spontaneous and Scripted Speech for Multilingual Audio](https://mlsanity.com/paper/2412.11896/) (2024)
- [Beyond Words: Multimodal LLM Knows When to Speak](https://mlsanity.com/paper/2505.14654/) (2025)
- [Measuring Entrainment in Spontaneous Code-switched Speech](https://mlsanity.com/paper/2311.07703/) (2023)
- [Reference Games as a Testbed for the Alignment of Model Uncertainty and Clarification Requests](https://mlsanity.com/paper/2601.07820/) (2026)
- [Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models](https://mlsanity.com/paper/2509.20237/) (2025)
- [Evaluating Language Models in Realistic Conversational Contexts](https://mlsanity.com/paper/2608.26131/) (2026)

## Abstract (arXiv)

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.