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Authors: Andrew Mackey ; B. Israel Cuevas and Susan Gauch

Affiliation: Computer Science and Engineering, University of Arkansas, Fayetteville, Arkansas, U.S.A.

Keyword(s): Emotion Analysis, Language Models, Natural Language Processing.

Abstract: Emotion recognition in conversation (ERC) is the task of classifying the emotion of each utterance in a conversation while learning the underlying latent representations. However, the representations for utterances are challenging to produce effectively given semantic and contextual information in the conversation. Large Language Models (LLMs) have demonstrated performance in various forms of emotion classification, including in zero-shot and few-shot settings, but their usage may be curtailed in some settings, particularly in limited resource environments. In this work, we propose a contrastive learning framework for the ERC task that leverages emotional anchors with semantic information encoded from an LLM to facilitate the learning of representations using a lightweight pretrained langauge model (PLM). Experimental results on benchmark ERC datasets demonstrate the effectiveness of our approach to baseline models while simultaneously reducing the inference cost of LLMs.

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Paper citation in several formats:
Mackey, A., Cuevas, B. I. and Gauch, S. (2025). Contrastive Learning for Conversational Emotion Recognition Using Knowledge Enhancement of Large Language Models. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR; ISBN 978-989-758-769-6; ISSN 2184-3228, SciTePress, pages 330-336. DOI: 10.5220/0013720100004000

@conference{kdir25,
author={Andrew Mackey and B. Israel Cuevas and Susan Gauch},
title={Contrastive Learning for Conversational Emotion Recognition Using Knowledge Enhancement of Large Language Models},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR},
year={2025},
pages={330-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013720100004000},
isbn={978-989-758-769-6},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR
TI - Contrastive Learning for Conversational Emotion Recognition Using Knowledge Enhancement of Large Language Models
SN - 978-989-758-769-6
IS - 2184-3228
AU - Mackey, A.
AU - Cuevas, B.
AU - Gauch, S.
PY - 2025
SP - 330
EP - 336
DO - 10.5220/0013720100004000
PB - SciTePress

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