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    <abstract>Social chatbots have gained immense popularity, and their appeal lies not just in their capacity to respond to the diverse requests from users, but also in the ability to develop an emotional connection with users. To further develop and promote social chatbots, we need to concentrate on increasing user interaction and take into account both the intellectual and emotional quotient in the conversational agents. Therefore, in this work, we propose the task of sentiment aware emotion controlled personalized dialogue generation giving the machine the capability to respond emotionally and in accordance with the persona of the user. As sentiment and emotions are highly co-related, we use the sentiment knowledge of the previous utterance to generate the correct emotional response in accordance with the user persona. We design a Transformer based Dialogue Generation framework, that generates responses that are sensitive to the emotion of the user and corresponds to the persona and sentiment as well. Moreover, the persona information is encoded by a different Transformer encoder, along with the dialogue history, is fed to the decoder for generating responses. We annotate the PersonaChat dataset with sentiment information to improve the response quality. Experimental results on the PersonaChat dataset show that the proposed framework significantly outperforms the existing baselines, thereby generating personalized emotional responses in accordance with the sentiment that provides better emotional connection and user satisfaction as desired in a social chatbot.</abstract>
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%0 Conference Proceedings
%T SEPRG: Sentiment aware Emotion controlled Personalized Response Generation
%A Firdaus, Mauajama
%A Jain, Umang
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F firdaus-etal-2021-seprg
%X Social chatbots have gained immense popularity, and their appeal lies not just in their capacity to respond to the diverse requests from users, but also in the ability to develop an emotional connection with users. To further develop and promote social chatbots, we need to concentrate on increasing user interaction and take into account both the intellectual and emotional quotient in the conversational agents. Therefore, in this work, we propose the task of sentiment aware emotion controlled personalized dialogue generation giving the machine the capability to respond emotionally and in accordance with the persona of the user. As sentiment and emotions are highly co-related, we use the sentiment knowledge of the previous utterance to generate the correct emotional response in accordance with the user persona. We design a Transformer based Dialogue Generation framework, that generates responses that are sensitive to the emotion of the user and corresponds to the persona and sentiment as well. Moreover, the persona information is encoded by a different Transformer encoder, along with the dialogue history, is fed to the decoder for generating responses. We annotate the PersonaChat dataset with sentiment information to improve the response quality. Experimental results on the PersonaChat dataset show that the proposed framework significantly outperforms the existing baselines, thereby generating personalized emotional responses in accordance with the sentiment that provides better emotional connection and user satisfaction as desired in a social chatbot.
%R 10.18653/v1/2021.inlg-1.39
%U https://aclanthology.org/2021.inlg-1.39
%U https://doi.org/10.18653/v1/2021.inlg-1.39
%P 353-363

Markdown (Informal)

[SEPRG: Sentiment aware Emotion controlled Personalized Response Generation](https://aclanthology.org/2021.inlg-1.39) (Firdaus et al., INLG 2021)

• SEPRG: Sentiment aware Emotion controlled Personalized Response Generation (Firdaus et al., INLG 2021)

ACL

• Mauajama Firdaus, Umang Jain, Asif Ekbal, and Pushpak Bhattacharyya. 2021. SEPRG: Sentiment aware Emotion controlled Personalized Response Generation. In Proceedings of the 14th International Conference on Natural Language Generation, pages 353–363, Aberdeen, Scotland, UK. Association for Computational Linguistics.