@inproceedings{mori-2024-cognitive,
title = "Cognitive Model of Listener Response Generation and Its Application to Dialogue Systems",
author = "Mori, Taiga",
editor = "Inoue, Koji and
Fu, Yahui and
Axelsson, Agnes and
Ohashi, Atsumoto and
Madureira, Brielen and
Zenimoto, Yuki and
Mohapatra, Biswesh and
Stricker, Armand and
Khosla, Sopan",
booktitle = "Proceedings of the 20th Workshop of Young Researchers' Roundtable on Spoken Dialogue Systems",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.yrrsds-1.15",
pages = "40--42",
abstract = "In this position paper, we introduce our efforts in modeling listener response generation and its application to dialogue systems. We propose that the cognitive process of generating listener responses involves four levels: attention level, word level, propositional information level, and activity level, with different types of responses used depending on the level. Attention level responses indicate that the listener is listening to and paying attention to the speaker{'}s speech. Word-level responses demonstrate the listener{'}s knowledge or understanding of a single representation. Propositional information level responses indicate the listener{'}s understanding, empathy, and emotions towards a single propositional information. Activity level responses are oriented towards activities. Additionally, we briefly report on our current initiative in generating propositional information level responses using a knowledge graph and LLMs.",
}
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<abstract>In this position paper, we introduce our efforts in modeling listener response generation and its application to dialogue systems. We propose that the cognitive process of generating listener responses involves four levels: attention level, word level, propositional information level, and activity level, with different types of responses used depending on the level. Attention level responses indicate that the listener is listening to and paying attention to the speaker’s speech. Word-level responses demonstrate the listener’s knowledge or understanding of a single representation. Propositional information level responses indicate the listener’s understanding, empathy, and emotions towards a single propositional information. Activity level responses are oriented towards activities. Additionally, we briefly report on our current initiative in generating propositional information level responses using a knowledge graph and LLMs.</abstract>
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%0 Conference Proceedings
%T Cognitive Model of Listener Response Generation and Its Application to Dialogue Systems
%A Mori, Taiga
%Y Inoue, Koji
%Y Fu, Yahui
%Y Axelsson, Agnes
%Y Ohashi, Atsumoto
%Y Madureira, Brielen
%Y Zenimoto, Yuki
%Y Mohapatra, Biswesh
%Y Stricker, Armand
%Y Khosla, Sopan
%S Proceedings of the 20th Workshop of Young Researchers’ Roundtable on Spoken Dialogue Systems
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F mori-2024-cognitive
%X In this position paper, we introduce our efforts in modeling listener response generation and its application to dialogue systems. We propose that the cognitive process of generating listener responses involves four levels: attention level, word level, propositional information level, and activity level, with different types of responses used depending on the level. Attention level responses indicate that the listener is listening to and paying attention to the speaker’s speech. Word-level responses demonstrate the listener’s knowledge or understanding of a single representation. Propositional information level responses indicate the listener’s understanding, empathy, and emotions towards a single propositional information. Activity level responses are oriented towards activities. Additionally, we briefly report on our current initiative in generating propositional information level responses using a knowledge graph and LLMs.
%U https://aclanthology.org/2024.yrrsds-1.15
%P 40-42
Markdown (Informal)
[Cognitive Model of Listener Response Generation and Its Application to Dialogue Systems](https://aclanthology.org/2024.yrrsds-1.15) (Mori, YRRSDS-WS 2024)
ACL