@inproceedings{priya-etal-2024-trip,
title = "{TRIP} {NEGOTIATOR}: A Travel Persona-aware Reinforced Dialogue Generation Model for Personalized Integrative Negotiation in Tourism",
author = "Priya, Priyanshu and
Yasheshbhai, Desai Vishesh and
Joshi, Ratnesh Kumar and
Ramnani, Roshni and
Maitra, Anutosh and
Sengupta, Shubhashis and
Ekbal, Asif",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.967/",
doi = "10.18653/v1/2024.findings-emnlp.967",
pages = "16566--16595",
abstract = "A sophisticated negotiation dialogue system for tourism should engage in negotiations beyond mere price considerations, encompassing various other aspects and amenities inherent in the tourism package. To ensure such tailored interaction, it is imperative to understand the intricacies of traveler preferences, constraints, and expectations. Incorporating these personality facets allows for customizing negotiation strategies, resulting in a more personalized and integrative experience. With this aim, we take a pivotal step in advancing automated dialogue systems for personalized integrative negotiation tasks. We develop DEAL, a pioneering Dialogue datasEt for personALized integrative negotiation task in the tourism domain. Further, we propose TRIP NEGOTIATOR, a novel Travel persona-aware Reinforced dIalogue generation model for Personalized iNtegrative nEGOTIATion within the tOuRism domain. TRIP NEGOTIATOR is built to discern the traveler`s persona and intent, systematically adjusts negotiation strategies, and directs the negotiation toward a pertinent phase to ensure effective negotiation. Through reinforcement learning with Proximal Policy Optimization (PPO), we guide TRIP NEGOTIATOR to generate coherent and diverse responses consistent with the traveler`s personality. Extensive qualitative and quantitative analyses demonstrate the effectiveness of TRIP NEGOTIATOR in generating personalized responses during negotiation."
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<abstract>A sophisticated negotiation dialogue system for tourism should engage in negotiations beyond mere price considerations, encompassing various other aspects and amenities inherent in the tourism package. To ensure such tailored interaction, it is imperative to understand the intricacies of traveler preferences, constraints, and expectations. Incorporating these personality facets allows for customizing negotiation strategies, resulting in a more personalized and integrative experience. With this aim, we take a pivotal step in advancing automated dialogue systems for personalized integrative negotiation tasks. We develop DEAL, a pioneering Dialogue datasEt for personALized integrative negotiation task in the tourism domain. Further, we propose TRIP NEGOTIATOR, a novel Travel persona-aware Reinforced dIalogue generation model for Personalized iNtegrative nEGOTIATion within the tOuRism domain. TRIP NEGOTIATOR is built to discern the traveler‘s persona and intent, systematically adjusts negotiation strategies, and directs the negotiation toward a pertinent phase to ensure effective negotiation. Through reinforcement learning with Proximal Policy Optimization (PPO), we guide TRIP NEGOTIATOR to generate coherent and diverse responses consistent with the traveler‘s personality. Extensive qualitative and quantitative analyses demonstrate the effectiveness of TRIP NEGOTIATOR in generating personalized responses during negotiation.</abstract>
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%0 Conference Proceedings
%T TRIP NEGOTIATOR: A Travel Persona-aware Reinforced Dialogue Generation Model for Personalized Integrative Negotiation in Tourism
%A Priya, Priyanshu
%A Yasheshbhai, Desai Vishesh
%A Joshi, Ratnesh Kumar
%A Ramnani, Roshni
%A Maitra, Anutosh
%A Sengupta, Shubhashis
%A Ekbal, Asif
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F priya-etal-2024-trip
%X A sophisticated negotiation dialogue system for tourism should engage in negotiations beyond mere price considerations, encompassing various other aspects and amenities inherent in the tourism package. To ensure such tailored interaction, it is imperative to understand the intricacies of traveler preferences, constraints, and expectations. Incorporating these personality facets allows for customizing negotiation strategies, resulting in a more personalized and integrative experience. With this aim, we take a pivotal step in advancing automated dialogue systems for personalized integrative negotiation tasks. We develop DEAL, a pioneering Dialogue datasEt for personALized integrative negotiation task in the tourism domain. Further, we propose TRIP NEGOTIATOR, a novel Travel persona-aware Reinforced dIalogue generation model for Personalized iNtegrative nEGOTIATion within the tOuRism domain. TRIP NEGOTIATOR is built to discern the traveler‘s persona and intent, systematically adjusts negotiation strategies, and directs the negotiation toward a pertinent phase to ensure effective negotiation. Through reinforcement learning with Proximal Policy Optimization (PPO), we guide TRIP NEGOTIATOR to generate coherent and diverse responses consistent with the traveler‘s personality. Extensive qualitative and quantitative analyses demonstrate the effectiveness of TRIP NEGOTIATOR in generating personalized responses during negotiation.
%R 10.18653/v1/2024.findings-emnlp.967
%U https://aclanthology.org/2024.findings-emnlp.967/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.967
%P 16566-16595
Markdown (Informal)
[TRIP NEGOTIATOR: A Travel Persona-aware Reinforced Dialogue Generation Model for Personalized Integrative Negotiation in Tourism](https://aclanthology.org/2024.findings-emnlp.967/) (Priya et al., Findings 2024)
ACL
- Priyanshu Priya, Desai Vishesh Yasheshbhai, Ratnesh Kumar Joshi, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta, and Asif Ekbal. 2024. TRIP NEGOTIATOR: A Travel Persona-aware Reinforced Dialogue Generation Model for Personalized Integrative Negotiation in Tourism. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 16566–16595, Miami, Florida, USA. Association for Computational Linguistics.