TRIP NEGOTIATOR : A Travel Persona-aware Reinforced Dialogue Generation Model for Personalized Integrative Negotiation in Tourism (2024)

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.

## dataset to develop the personalized trip negotiator, we crafted deal, an innovative dialogue dataset for personalized integrative negotiation task. we focus on the dialogues considering the traveler's persona information and requirements to facilitate the best travel packages and deals. the ultimate goal is to offer a seamless and collaborative planning and booking experience during negotiation, thereby contributing to an enriched and positively transformed landscape within the realm of tourism. ## proposed trip negotiator to facilitate effective negotiation between the dialogue agent and travelers having unique personas and intentions, we introduce trip negotiator, a novel travel persona-aware reinforced dialogue generation model for personalized integrative negotiation within tourism domain. the architecture of trip negotiator is depicted in figure 2 . it is built in three stages, viz. supervised finetuning, reward modeling, and rl fine-tuning. ## experimental details baselines. we evaluate trip negotiator against eight baselines, viz. gpt-2-small @xcite , dialogpt-small @xcite , ardm @xcite , gpt-critic @xcite evaluation metrics. we conduct automatic and human evaluations to assess the performance of the trip negotiator. we evaluate classifiers using weighted accuracy (w-acc.) and macro-f1, and intent-phase generation model using perplexity (ppl) @xcite , bleu @xcite , meteor @xcite , and bertscore-f1 (bs-f1) @xcite . trip negotiator is evaluated using traitfocused metrics (tfm) to measure the travel agent's proximity to the goal from the perspective of persona modeling and negotiation, and language-focused metrics (lfm) to emphasize the language realization and generation ability for negotiation dialogue. for automatic evaluation, persona alignment (pa), negotiation strategy con-sistency (nsc), and negotiation phase agreement (npa) foot_6 are used as tfm, and ppl, bleu, me-teor, bs-f1, and response length (r-len) are used as lfm. for human evaluation, besides pa, nsc and npa, win times (wt), deal rate (dr), and pareto optimality (po) @xcite are utilized as tfm, and fluency (f), contextual coherence (cc), and engagingness (e) are used as lfm. pa, nsc, npa, f, cc, and e are rated on a scale of 1-5 (the higher, the better). the details of evaluation metrics and human evaluation process are given in §c.5 of the appendix. ## results and analysis further, the proposed system yields 0.35, 0.87, and 0.81 scores of bleu, meteor, and bs-f1, respectively, with an increase of +0.07, +0.12, and +0.08 points compared to psft. these scores signify high lexical and semantic similarity of generated responses with the gold responses. this strengthens the design of rewards in generating customized responses aligned with gold responses. it can also be deduced that the performance of trip negotiator decreases when any of the rewards are removed. notably, the ppl score increases in the order trip negotiator -(@xmath0 the performance further drops when either all the tfr, or lfr, or both are ablated. these results signify the need for both tfr and lfr to generate fluent, coherent, and diverse responses grounded in appropriate negotiation strategy and phase and aligned with the traveler's persona and intents. due to space constraints, results of classifiers and intent-phase model are given in §c.3 and §c.4 of the appendix. ## conclusion this work introduces a new task of personalized integrative response generation considering the travelers' travel persona and requirements in negotiation conversations. for this task, a novel dialogue dataset deal is created by prompting mpt-7b with manual interventions and is annotated with travel persona, intents, negotiation strategies, and phases. then, a novel personalized integrative negotiation dialogue system, trip negotiator, is developed in an rl framework by designing unique rewards to facilitate the generation of travel persona-aware responses with appropriate negotiation strategy and negotiation phase aligned with the pertinent intent. the findings emphasize the necessity of each reward to facilitate a personalized integrative experience during negotiation.