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        <title>RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System</title>
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        <namePart type="given">Zishan</namePart>
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    <name type="personal">
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            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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    <abstract>Although there has been a plethora of work on open-domain conversational systems, most of the systems lack the mechanism of controlling the concept transitions in a dialogue. For activities like switching from casual chit-chat to task-oriented conversation, an agent with the ability to manage the flow of concepts in a conversation might be helpful. The user would find the dialogue more engaging and be more receptive to such transitions if these concept transitions were made while taking into account the user’s persona. Focusing on persona-aware concept transitions, we propose a Reinforced Persona-aware Topic-guiding Conversational System (RPTCS). Due to the lack of a persona-aware topic transition dataset, we propose a novel conversation dataset creation mechanism in which the conversational agent leads the discourse to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation. To avoid scarcely available expensive human resource, the entire data-creation process is mostly automatic with human-in-loop only for quality checks. This created conversational dataset named PTCD is used to develop the RPTCS in two steps. First, a maximum likelihood estimation loss-based conversational model is trained on PTCD. Then this trained model is fine-tuned in a Reinforcement Learning (RL) framework by employing novel reward functions to assure persona, topic, and context consistency with non-repetitiveness in generated responses. Our experimental results demonstrate the strength of the proposed system with respect to strong baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2023.eacl-main.253</identifier>
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        <date>2023-05</date>
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            <start>3482</start>
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%0 Conference Proceedings
%T RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System
%A Ahmad, Zishan
%A Mishra, Kshitij
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F ahmad-etal-2023-rptcs
%X Although there has been a plethora of work on open-domain conversational systems, most of the systems lack the mechanism of controlling the concept transitions in a dialogue. For activities like switching from casual chit-chat to task-oriented conversation, an agent with the ability to manage the flow of concepts in a conversation might be helpful. The user would find the dialogue more engaging and be more receptive to such transitions if these concept transitions were made while taking into account the user’s persona. Focusing on persona-aware concept transitions, we propose a Reinforced Persona-aware Topic-guiding Conversational System (RPTCS). Due to the lack of a persona-aware topic transition dataset, we propose a novel conversation dataset creation mechanism in which the conversational agent leads the discourse to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation. To avoid scarcely available expensive human resource, the entire data-creation process is mostly automatic with human-in-loop only for quality checks. This created conversational dataset named PTCD is used to develop the RPTCS in two steps. First, a maximum likelihood estimation loss-based conversational model is trained on PTCD. Then this trained model is fine-tuned in a Reinforcement Learning (RL) framework by employing novel reward functions to assure persona, topic, and context consistency with non-repetitiveness in generated responses. Our experimental results demonstrate the strength of the proposed system with respect to strong baselines.
%R 10.18653/v1/2023.eacl-main.253
%U https://aclanthology.org/2023.eacl-main.253
%U https://doi.org/10.18653/v1/2023.eacl-main.253
%P 3482-3494

Markdown (Informal)

[RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System](https://aclanthology.org/2023.eacl-main.253) (Ahmad et al., EACL 2023)

• RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System (Ahmad et al., EACL 2023)

ACL

• Zishan Ahmad, Kshitij Mishra, Asif Ekbal, and Pushpak Bhattacharyya. 2023. RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3482–3494, Dubrovnik, Croatia. Association for Computational Linguistics.