Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis

• URL: http://arxiv.org/abs/2309.15656v2
• Date: Thu, 03 Oct 2024 14:27:14 GMT
• Title: Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis
• Authors: Ildikó Pilán, Laurent Prévot, Hendrik Buschmeier, Pierre Lison,
• Abstract summary: We show that communicative feedback is markedly less frequent in subtitles than in spontaneous dialogues. We also show that dialogues generated by standard LLMs lie much closer to scripted dialogues than spontaneous interactions.
• Score: 3.3436261794502657
• License:
• Abstract: 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.

Related papers

• Are LLMs Robust for Spoken Dialogues? [10.855403629160921]
  Large Pre-Trained Language Models have demonstrated state-of-the-art performance in different downstream tasks. Most of the publicly available datasets and benchmarks on task-oriented dialogues focus on written conversations. We have evaluated the performance of LLMs for spoken task-oriented dialogues on the DSTC11 test sets.
  arXiv  Detail & Related papers  (2024-01-04T14:36:38Z)
• Towards human-like spoken dialogue generation between AI agents from written dialogue [8.4989907582951]
  This study proposes CHATS - CHatty Agents Text-to-Speech - a discrete token-based system designed to generate spoken dialogues based on written dialogues. Our system can generate speech for both the speaker side and the listener side simultaneously, using only the transcription from the speaker side.
  arXiv  Detail & Related papers  (2023-10-02T11:03:20Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• Post-Training Dialogue Summarization using Pseudo-Paraphrasing [12.083992819138716]
  We propose to post-train pretrained language models (PLMs) to rephrase from dialogue to narratives. Comprehensive experiments show that our approach significantly improves vanilla PLMs on dialogue summarization.
  arXiv  Detail & Related papers  (2022-04-28T13:42:19Z)
• Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling [80.51094098799736]
  We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder. BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
  arXiv  Detail & Related papers  (2022-04-18T03:51:46Z)
• Who says like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning [2.251583286448503]
  We focus on the association between utterances from individual speakers and unique syntactic structures. Speakers have unique textual styles that can contain linguistic information, such as voiceprint. We employ multi-task learning of both syntax-aware information and dialogue summarization.
  arXiv  Detail & Related papers  (2021-09-29T05:30:39Z)
• "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations [87.95711406978157]
  This work presents a new benchmark on spoken task-oriented conversations. We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
  arXiv  Detail & Related papers  (2021-09-28T04:51:04Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)
• Rethinking Dialogue State Tracking with Reasoning [76.0991910623001]
  This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method significantly outperforms the state-of-the-art methods by 38.6% in terms of joint belief accuracy for MultiWOZ 2.1.
  arXiv  Detail & Related papers  (2020-05-27T02:05:33Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)
• Interview: A Large-Scale Open-Source Corpus of Media Dialog [11.28504775964698]
  We introduce 'Interview': a large-scale (105K conversations) media dialog dataset collected from news interview transcripts. Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance. 'Interview' contains speaker role annotations for each turn, facilitating the development of engaging, responsive dialog systems.
  arXiv  Detail & Related papers  (2020-04-07T02:44:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.