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        <title>Forecasting Implicit Emotions Elicited in Conversations</title>
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    <abstract>This paper aims to forecast the implicit emotion elicited in the dialogue partner by a textual input utterance. Forecasting the interlocutor’s emotion is beneficial for natural language generation in dialogue systems to avoid generating utterances that make the users uncomfortable. Previous studies forecast the emotion conveyed in the interlocutor’s response, assuming it will explicitly reflect their elicited emotion. However, true emotions are not always expressed verbally. We propose a new task to directly forecast the implicit emotion elicited by an input utterance, which does not rely on this assumption. We compare this task with related ones to investigate the impact of dialogue history and one’s own utterance on predicting explicit and implicit emotions. Our result highlights the importance of dialogue history for predicting implicit emotions. It also reveals that, unlike explicit emotions, implicit emotions show limited improvement in predictive performance with one’s own utterance, and that they are more difficult to predict than explicit emotions. We find that even a large language model (LLM) struggles to forecast implicit emotions accurately.</abstract>
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%0 Conference Proceedings
%T Forecasting Implicit Emotions Elicited in Conversations
%A Koga, Yurie
%A Kando, Shunsuke
%A Miyao, Yusuke
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F koga-etal-2024-forecasting-implicit
%X This paper aims to forecast the implicit emotion elicited in the dialogue partner by a textual input utterance. Forecasting the interlocutor’s emotion is beneficial for natural language generation in dialogue systems to avoid generating utterances that make the users uncomfortable. Previous studies forecast the emotion conveyed in the interlocutor’s response, assuming it will explicitly reflect their elicited emotion. However, true emotions are not always expressed verbally. We propose a new task to directly forecast the implicit emotion elicited by an input utterance, which does not rely on this assumption. We compare this task with related ones to investigate the impact of dialogue history and one’s own utterance on predicting explicit and implicit emotions. Our result highlights the importance of dialogue history for predicting implicit emotions. It also reveals that, unlike explicit emotions, implicit emotions show limited improvement in predictive performance with one’s own utterance, and that they are more difficult to predict than explicit emotions. We find that even a large language model (LLM) struggles to forecast implicit emotions accurately.
%U https://aclanthology.org/2024.inlg-main.12
%P 145-152

Markdown (Informal)

[Forecasting Implicit Emotions Elicited in Conversations](https://aclanthology.org/2024.inlg-main.12) (Koga et al., INLG 2024)

• Forecasting Implicit Emotions Elicited in Conversations (Koga et al., INLG 2024)

ACL

• Yurie Koga, Shunsuke Kando, and Yusuke Miyao. 2024. Forecasting Implicit Emotions Elicited in Conversations. In Proceedings of the 17th International Natural Language Generation Conference, pages 145–152, Tokyo, Japan. Association for Computational Linguistics.