@inproceedings{maes-etal-2024-get,
title = "Did You Get It? A Zero-Shot Approach to Locate Information Transfers in Conversations",
author = {Ma{\"e}s, Eliot and
Boudraa, Hossam and
Blache, Philippe and
Becerra-Bonache, Leonor},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.437",
pages = "4877--4890",
abstract = "Interaction theories suggest that the emergence of mutual understanding between speakers in natural conversations depends on the construction of a shared knowledge base (\textit{common ground}), but the details of which information and the circumstances under which it is memorized are not explained by any model. Previous works have looked at metrics derived from Information Theory to quantify the dynamics of information exchanged between participants, but do not provide an efficient way to locate information that will enter the common ground. We propose a new method based on the segmentation of a conversation into themes followed by their summarization. We then obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. We evaluate two Large Language Models (LLMs) on this pipeline, on the French conversational corpus Paco-Cheese. More generally, we explore how the recent developments in the field of LLMs provide us with the means to implement these new methods and more generally support research into questions that usually heavily relies on human annotators.",
}
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<abstract>Interaction theories suggest that the emergence of mutual understanding between speakers in natural conversations depends on the construction of a shared knowledge base (common ground), but the details of which information and the circumstances under which it is memorized are not explained by any model. Previous works have looked at metrics derived from Information Theory to quantify the dynamics of information exchanged between participants, but do not provide an efficient way to locate information that will enter the common ground. We propose a new method based on the segmentation of a conversation into themes followed by their summarization. We then obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. We evaluate two Large Language Models (LLMs) on this pipeline, on the French conversational corpus Paco-Cheese. More generally, we explore how the recent developments in the field of LLMs provide us with the means to implement these new methods and more generally support research into questions that usually heavily relies on human annotators.</abstract>
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%0 Conference Proceedings
%T Did You Get It? A Zero-Shot Approach to Locate Information Transfers in Conversations
%A Maës, Eliot
%A Boudraa, Hossam
%A Blache, Philippe
%A Becerra-Bonache, Leonor
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F maes-etal-2024-get
%X Interaction theories suggest that the emergence of mutual understanding between speakers in natural conversations depends on the construction of a shared knowledge base (common ground), but the details of which information and the circumstances under which it is memorized are not explained by any model. Previous works have looked at metrics derived from Information Theory to quantify the dynamics of information exchanged between participants, but do not provide an efficient way to locate information that will enter the common ground. We propose a new method based on the segmentation of a conversation into themes followed by their summarization. We then obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. We evaluate two Large Language Models (LLMs) on this pipeline, on the French conversational corpus Paco-Cheese. More generally, we explore how the recent developments in the field of LLMs provide us with the means to implement these new methods and more generally support research into questions that usually heavily relies on human annotators.
%U https://aclanthology.org/2024.lrec-main.437
%P 4877-4890
Markdown (Informal)
[Did You Get It? A Zero-Shot Approach to Locate Information Transfers in Conversations](https://aclanthology.org/2024.lrec-main.437) (Maës et al., LREC-COLING 2024)
ACL