@inproceedings{tian-etal-2022-empathetic,
title = "Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory",
author = "Tian, Zhiliang and
Wang, Yinliang and
Song, Yiping and
Zhang, Chi and
Lee, Dongkyu and
Zhao, Yingxiu and
Li, Dongsheng and
Zhang, Nevin L.",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.475",
doi = "10.18653/v1/2022.findings-emnlp.475",
pages = "6364--6376",
abstract = "Emotional conversation systems generate responses for the input queries considering the speaker{'}s emotions in a conversation. Existing emotional conversation systems output emotional responses according to either a given emotion or the user{'}s emotion reflected in the input queries. Following a given emotion may lead to an emotional drift between the given emotion and the conversation state, and following only the user{'}s emotion may aggravate the user{'}s negative feelings if users suffer from a negative mood. In this paper, we propose to generate empathetic responses catering to the user{'}s emotions while leading the conversation to be emotionally positive. Particularly, by abstracting the conversation corpus, we extract and store the different responding strategies for different users{'} emotions and conversational topics into a memory. We encourage positive emotions in conversation via a sentiment evaluator. We model the memory outputs with a Gaussian mixture distribution and sample a final responding strategy from the distribution. The strategy acts as a condition to a transformer model to generate responses. The experiments verify our model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses.",
}
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<abstract>Emotional conversation systems generate responses for the input queries considering the speaker’s emotions in a conversation. Existing emotional conversation systems output emotional responses according to either a given emotion or the user’s emotion reflected in the input queries. Following a given emotion may lead to an emotional drift between the given emotion and the conversation state, and following only the user’s emotion may aggravate the user’s negative feelings if users suffer from a negative mood. In this paper, we propose to generate empathetic responses catering to the user’s emotions while leading the conversation to be emotionally positive. Particularly, by abstracting the conversation corpus, we extract and store the different responding strategies for different users’ emotions and conversational topics into a memory. We encourage positive emotions in conversation via a sentiment evaluator. We model the memory outputs with a Gaussian mixture distribution and sample a final responding strategy from the distribution. The strategy acts as a condition to a transformer model to generate responses. The experiments verify our model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses.</abstract>
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%0 Conference Proceedings
%T Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory
%A Tian, Zhiliang
%A Wang, Yinliang
%A Song, Yiping
%A Zhang, Chi
%A Lee, Dongkyu
%A Zhao, Yingxiu
%A Li, Dongsheng
%A Zhang, Nevin L.
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F tian-etal-2022-empathetic
%X Emotional conversation systems generate responses for the input queries considering the speaker’s emotions in a conversation. Existing emotional conversation systems output emotional responses according to either a given emotion or the user’s emotion reflected in the input queries. Following a given emotion may lead to an emotional drift between the given emotion and the conversation state, and following only the user’s emotion may aggravate the user’s negative feelings if users suffer from a negative mood. In this paper, we propose to generate empathetic responses catering to the user’s emotions while leading the conversation to be emotionally positive. Particularly, by abstracting the conversation corpus, we extract and store the different responding strategies for different users’ emotions and conversational topics into a memory. We encourage positive emotions in conversation via a sentiment evaluator. We model the memory outputs with a Gaussian mixture distribution and sample a final responding strategy from the distribution. The strategy acts as a condition to a transformer model to generate responses. The experiments verify our model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses.
%R 10.18653/v1/2022.findings-emnlp.475
%U https://aclanthology.org/2022.findings-emnlp.475
%U https://doi.org/10.18653/v1/2022.findings-emnlp.475
%P 6364-6376
Markdown (Informal)
[Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory](https://aclanthology.org/2022.findings-emnlp.475) (Tian et al., Findings 2022)
ACL
- Zhiliang Tian, Yinliang Wang, Yiping Song, Chi Zhang, Dongkyu Lee, Yingxiu Zhao, Dongsheng Li, and Nevin L. Zhang. 2022. Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6364–6376, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.