@inproceedings{nargund-etal-2022-par,
title = "{PAR}: Persona Aware Response in Conversational Systems",
author = "Nargund, Abhijit and
Pandey, Sandeep and
Ham, Jina",
editor = "Akhtar, Md. Shad and
Chakraborty, Tanmoy",
booktitle = "Proceedings of the 19th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2022",
address = "New Delhi, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.icon-main.6",
pages = "50--54",
abstract = "To make the Human Computer Interaction more user friendly and persona aligned, detection of user persona is of utmost significance. Towards achieving this objective, we describe a novel approach to select the persona of a user from pre-determine list of personas and utilize it to generate personalized responses. This is achieved in two steps. Firstly, closest matching persona is detected from a set of pre-determined persona for the user. The second step involves the use of a fine-tuned natural language generation (NLG) model to generate persona compliant responses. Through experiments, we demonstrate that the proposed architecture generates better responses than current approaches by using a detected persona. Experimental evaluation on the PersonaChat dataset has demonstrated notable performance in terms of perplexity and F1-score.",
}
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%0 Conference Proceedings
%T PAR: Persona Aware Response in Conversational Systems
%A Nargund, Abhijit
%A Pandey, Sandeep
%A Ham, Jina
%Y Akhtar, Md. Shad
%Y Chakraborty, Tanmoy
%S Proceedings of the 19th International Conference on Natural Language Processing (ICON)
%D 2022
%8 December
%I Association for Computational Linguistics
%C New Delhi, India
%F nargund-etal-2022-par
%X To make the Human Computer Interaction more user friendly and persona aligned, detection of user persona is of utmost significance. Towards achieving this objective, we describe a novel approach to select the persona of a user from pre-determine list of personas and utilize it to generate personalized responses. This is achieved in two steps. Firstly, closest matching persona is detected from a set of pre-determined persona for the user. The second step involves the use of a fine-tuned natural language generation (NLG) model to generate persona compliant responses. Through experiments, we demonstrate that the proposed architecture generates better responses than current approaches by using a detected persona. Experimental evaluation on the PersonaChat dataset has demonstrated notable performance in terms of perplexity and F1-score.
%U https://aclanthology.org/2022.icon-main.6
%P 50-54
Markdown (Informal)
[PAR: Persona Aware Response in Conversational Systems](https://aclanthology.org/2022.icon-main.6) (Nargund et al., ICON 2022)
ACL
- Abhijit Nargund, Sandeep Pandey, and Jina Ham. 2022. PAR: Persona Aware Response in Conversational Systems. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 50–54, New Delhi, India. Association for Computational Linguistics.