From "um" to "yeah": Producing, predicting, and regulating information flow in human conversation

• URL: http://arxiv.org/abs/2403.08890v1
• Date: Wed, 13 Mar 2024 18:20:24 GMT
• Title: From "um" to "yeah": Producing, predicting, and regulating information flow in human conversation
• Authors: Claire Augusta Bergey, Simon DeDeo,
• Abstract summary: We estimate the information density of unstructured conversation, of approximately 13 bits/second. We find significant effects associated with the cognitive load of both retrieving, and presenting, that information. Our results provide new insights into theories of how we respond to fluctuating demands on cognitive resources.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversation demands attention. Speakers must call words to mind, listeners must make sense of them, and both together must negotiate this flow of information, all in fractions of a second. We used large language models to study how this works in a large-scale dataset of English-language conversation, the CANDOR corpus. We provide a new estimate of the information density of unstructured conversation, of approximately 13 bits/second, and find significant effects associated with the cognitive load of both retrieving, and presenting, that information. We also reveal a role for backchannels -- the brief yeahs, uh-huhs, and mhmms that listeners provide -- in regulating the production of novelty: the lead-up to a backchannel is associated with declining information rate, while speech downstream rebounds to previous rates. Our results provide new insights into long-standing theories of how we respond to fluctuating demands on cognitive resources, and how we negotiate those demands in partnership with others.

Related papers

• Talking Turns: Benchmarking Audio Foundation Models on Turn-Taking Dynamics [54.03209351287654]
  We propose a novel evaluation protocol that can assess spoken dialog system's turn-taking capabilities. We present the first comprehensive user study that evaluates existing spoken dialogue systems on their ability to perform turn-taking events. We will open source our evaluation platform to promote the development of advanced conversational AI systems.
  arXiv  Detail & Related papers  (2025-03-03T04:46:04Z)
• AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models [74.10293412011455]
  We propose AutoConv for synthetic conversation generation. Specifically, we formulate the conversation generation problem as a language modeling task. We finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process.
  arXiv  Detail & Related papers  (2023-08-12T08:52:40Z)
• NewsDialogues: Towards Proactive News Grounded Conversation [72.10055780635625]
  We propose a novel task, Proactive News Grounded Conversation, in which a dialogue system can proactively lead the conversation based on some key topics of the news. To further develop this novel task, we collect a human-to-human Chinese dialogue dataset tsNewsDialogues, which includes 1K conversations with a total of 14.6K utterances.
  arXiv  Detail & Related papers  (2023-08-12T08:33:42Z)
• Joining the Conversation: Towards Language Acquisition for Ad Hoc Team Play [1.370633147306388]
  We propose and consider the problem of cooperative language acquisition as a particular form of the ad hoc team play problem. We present a probabilistic model for inferring a speaker's intentions and a listener's semantics from observing communications between a team of language-users.
  arXiv  Detail & Related papers  (2023-05-20T16:59:27Z)
• Construction Repetition Reduces Information Rate in Dialogue [2.1104930506758275]
  We study the repetition of lexicalised constructions in English open-domain spoken dialogues. We observe that construction usage lowers the information content of utterances.
  arXiv  Detail & Related papers  (2022-10-15T15:44:00Z)
• End-to-end Spoken Conversational Question Answering: Task, Dataset and Model [92.18621726802726]
  In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. We propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows. Our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.
  arXiv  Detail & Related papers  (2022-04-29T17:56:59Z)
• Know Deeper: Knowledge-Conversation Cyclic Utilization Mechanism for Open-domain Dialogue Generation [11.72386584395626]
  End-to-End intelligent neural dialogue systems suffer from the problems of generating inconsistent and repetitive responses. Existing dialogue models pay attention to unilaterally incorporating personal knowledge into the dialog while ignoring the fact that incorporating the personality-related conversation information into personal knowledge taken as the bilateral information flow boosts the quality of the subsequent conversation. We propose a conversation-adaption multi-view persona aware response generation model that aims at enhancing conversation consistency and alleviating the repetition from two folds.
  arXiv  Detail & Related papers  (2021-07-16T08:59:06Z)
• Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
  We study the task of few-shot $textitlanguage coordination$. We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities. This requires the ability to model the partner's beliefs, a vital component of human communication.
  arXiv  Detail & Related papers  (2021-07-12T19:26:11Z)
• Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure [53.77234444565652]
  We identify the responding relations in the conversation discourse, which link response utterances to their initiations. We propose a model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts.
  arXiv  Detail & Related papers  (2021-04-17T17:46:00Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)

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.