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    <titleInfo>
        <title>A Taxonomy of Empathetic Response Intents in Human Social Conversations</title>
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    <name type="personal">
        <namePart type="given">Anuradha</namePart>
        <namePart type="family">Welivita</namePart>
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    <name type="personal">
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
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    <abstract>Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. One of the challenges is enabling them to converse in an empathetic manner. Current neural response generation methods rely solely on end-to-end learning from large scale conversation data to generate dialogues. This approach can produce socially unacceptable responses due to the lack of large-scale quality data used to train the neural models. However, recent work has shown the promise of combining dialogue act/intent modelling and neural response generation. This hybrid method improves the response quality of chatbots and makes them more controllable and interpretable. A key element in dialog intent modelling is the development of a taxonomy. Inspired by this idea, we have manually labeled 500 response intents using a subset of a sizeable empathetic dialogue dataset (25K dialogues). Our goal is to produce a large-scale taxonomy for empathetic response intents. Furthermore, using lexical and machine learning methods, we automatically analysed both speaker and listener utterances of the entire dataset with identified response intents and 32 emotion categories. Finally, we use information visualization methods to summarize emotional dialogue exchange patterns and their temporal progression. These results reveal novel and important empathy patterns in human-human open-domain conversations and can serve as heuristics for hybrid approaches.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.429</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T A Taxonomy of Empathetic Response Intents in Human Social Conversations
%A Welivita, Anuradha
%A Pu, Pearl
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F welivita-pu-2020-taxonomy
%X Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. One of the challenges is enabling them to converse in an empathetic manner. Current neural response generation methods rely solely on end-to-end learning from large scale conversation data to generate dialogues. This approach can produce socially unacceptable responses due to the lack of large-scale quality data used to train the neural models. However, recent work has shown the promise of combining dialogue act/intent modelling and neural response generation. This hybrid method improves the response quality of chatbots and makes them more controllable and interpretable. A key element in dialog intent modelling is the development of a taxonomy. Inspired by this idea, we have manually labeled 500 response intents using a subset of a sizeable empathetic dialogue dataset (25K dialogues). Our goal is to produce a large-scale taxonomy for empathetic response intents. Furthermore, using lexical and machine learning methods, we automatically analysed both speaker and listener utterances of the entire dataset with identified response intents and 32 emotion categories. Finally, we use information visualization methods to summarize emotional dialogue exchange patterns and their temporal progression. These results reveal novel and important empathy patterns in human-human open-domain conversations and can serve as heuristics for hybrid approaches.
%R 10.18653/v1/2020.coling-main.429
%U https://aclanthology.org/2020.coling-main.429
%U https://doi.org/10.18653/v1/2020.coling-main.429
%P 4886-4899

Markdown (Informal)

[A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://aclanthology.org/2020.coling-main.429) (Welivita & Pu, COLING 2020)

• A Taxonomy of Empathetic Response Intents in Human Social Conversations (Welivita & Pu, COLING 2020)

ACL

• Anuradha Welivita and Pearl Pu. 2020. A Taxonomy of Empathetic Response Intents in Human Social Conversations. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4886–4899, Barcelona, Spain (Online). International Committee on Computational Linguistics.