@inproceedings{hong-etal-2024-detectivenn,
title = "{D}etective{NN}: Imitating Human Emotional Reasoning with a Recall-Detect-Predict Framework for Emotion Recognition in Conversations",
author = "Hong, Simin and
Sun, Jun and
Li, Taihao",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.536/",
doi = "10.18653/v1/2024.findings-emnlp.536",
pages = "9170--9180",
abstract = "Emotion Recognition in conversations (ERC) involves an internal cognitive process that interprets emotional cues by using a collection of past emotional experiences. However, many existing methods struggle to decipher emotional cues in dialogues since they are insufficient in understanding the rich historical emotional context. In this work, we introduce an innovative Detective Network (DetectiveNN), a novel model that is grounded in the cognitive theory of emotion and utilizes a {\textquotedblleft}recall-detect-predict{\textquotedblright} framework to imitate human emotional reasoning. This process begins by {\textquoteleft}recalling' past interactions of a specific speaker to collect emotional cues. It then {\textquoteleft}detects' relevant emotional patterns by interpreting these cues in the context of the ongoing conversation. Finally, it {\textquoteleft}predicts' the speaker`s current emotional state. Tested on three benchmark datasets, our approach significantly outperforms existing methods. This highlights the advantages of incorporating cognitive factors into deep learning for ERC, enhancing task efficacy and prediction accuracy."
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<abstract>Emotion Recognition in conversations (ERC) involves an internal cognitive process that interprets emotional cues by using a collection of past emotional experiences. However, many existing methods struggle to decipher emotional cues in dialogues since they are insufficient in understanding the rich historical emotional context. In this work, we introduce an innovative Detective Network (DetectiveNN), a novel model that is grounded in the cognitive theory of emotion and utilizes a “recall-detect-predict” framework to imitate human emotional reasoning. This process begins by ‘recalling’ past interactions of a specific speaker to collect emotional cues. It then ‘detects’ relevant emotional patterns by interpreting these cues in the context of the ongoing conversation. Finally, it ‘predicts’ the speaker‘s current emotional state. Tested on three benchmark datasets, our approach significantly outperforms existing methods. This highlights the advantages of incorporating cognitive factors into deep learning for ERC, enhancing task efficacy and prediction accuracy.</abstract>
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%0 Conference Proceedings
%T DetectiveNN: Imitating Human Emotional Reasoning with a Recall-Detect-Predict Framework for Emotion Recognition in Conversations
%A Hong, Simin
%A Sun, Jun
%A Li, Taihao
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F hong-etal-2024-detectivenn
%X Emotion Recognition in conversations (ERC) involves an internal cognitive process that interprets emotional cues by using a collection of past emotional experiences. However, many existing methods struggle to decipher emotional cues in dialogues since they are insufficient in understanding the rich historical emotional context. In this work, we introduce an innovative Detective Network (DetectiveNN), a novel model that is grounded in the cognitive theory of emotion and utilizes a “recall-detect-predict” framework to imitate human emotional reasoning. This process begins by ‘recalling’ past interactions of a specific speaker to collect emotional cues. It then ‘detects’ relevant emotional patterns by interpreting these cues in the context of the ongoing conversation. Finally, it ‘predicts’ the speaker‘s current emotional state. Tested on three benchmark datasets, our approach significantly outperforms existing methods. This highlights the advantages of incorporating cognitive factors into deep learning for ERC, enhancing task efficacy and prediction accuracy.
%R 10.18653/v1/2024.findings-emnlp.536
%U https://aclanthology.org/2024.findings-emnlp.536/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.536
%P 9170-9180
Markdown (Informal)
[DetectiveNN: Imitating Human Emotional Reasoning with a Recall-Detect-Predict Framework for Emotion Recognition in Conversations](https://aclanthology.org/2024.findings-emnlp.536/) (Hong et al., Findings 2024)
ACL