DOI: 10.5281/zenodo.17368957
title: Multilingual Tourism Evaluation (MEA + CBTRS)

description: MCRS: Multilingual Context and Role-playing Strategy for Tourism Evaluation

Overview

MCRS (Multilingual Context and Role-playing Strategy) is a novel framework designed to enhance intelligent evaluation of cross-border tourism services through multilingual context modeling and role-playing generation.By integrating advanced Multimodal Encoder Architecture (MEA) and the Cross-Border Tourism Role-Playing Strategy (CBTRS), the system simulates real-world interactions between tourists and service providers, enabling culturally adaptive and linguistically aware service assessments.

MCRS aims to improve accuracy, cultural inclusivity, and contextual sensitivity, setting a new standard for intelligent tourism service evaluation.



✨ Features

Multilingual Context Modeling



Supports multiple languages simultaneously.

Uses context vectors and historical dialogue aggregation for multilingual interaction analysis.

Captures linguistic nuances and cultural subtleties in tourist-service interactions.


Role-Playing Generation



Simulates realistic dialogues between tourists and service providers.

Dynamically assigns roles (e.g., tourist, guide, hotel staff) to create culturally grounded scenarios.

Enables adaptive, scenario-based evaluation of service quality.


Multimodal Encoder Architecture (MEA)



Combines linguistic, cultural, and contextual data to model multilingual interactions.

Employs CSRM, Mamba, and EFL layers for high-fidelity feature representation (see Fig. 1, p.7).

Adaptive feedback loop enables real-time updates based on new interaction data.


Cross-Border Tourism Role-Playing Strategy (CBTRS)



Leverages graphical propagation and adaptive learning to generate dynamic scenarios (see Fig. 3, p.9).

Evaluates service quality using scenario-based scoring functions.

Integrates reinforcement learning with multilingual processing for continuous improvement.




📊 Datasets




Dataset
Description
Purpose




Multilingual Tourism Interaction Dataset
Multilingual dialogues between tourists and service providers
Context modeling


Cross-Border Service Evaluation Dataset
Reviews and feedback from international tourists
Service quality scoring


Role-Playing Strategy Tourism Dataset
Simulated interactions under role-playing conditions
Scenario-based training


Intelligent Contextual Modeling Dataset
Contextual information such as time, location, and cultural events
Adaptive evaluation






 




🚀 Usage



Predicted service evaluation scores

Role-playing simulation results

Contextual insights (linguistic & cultural)

Scenario-based recommendation




🧪 Applications



Real-time multilingual tourism service evaluation

Cultural context-aware travel assistance

Role-playing based training for tourism staff

Adaptive dialogue modeling for multilingual customer support




🧩 Model Components



Multimodal Encoder Architecture (MEA) — multilingual and contextual modeling (Fig. 1, p.7)

Cross-Border Tourism Role-Playing Strategy (CBTRS) — scenario generation & evaluation (Fig. 3, p.9)

Adaptive Feedback Mechanism — real-time updates based on tourist interactions (Eq. 8, p.8)

Graphical Propagation Layer — scenario refinement with reinforcement learning (Eq. 10, p.10).




📈 Performance




Dataset
Accuracy
Precision
Recall
AUC




Multilingual Tourism Interaction
89.45
88.36
87.78
88.00


Cross-Border Service Evaluation
91.12
90.03
89.46
89.68


Role-Playing Strategy Tourism
89.12
88.01
87.34
87.67


Intelligent Contextual Modeling
91.23
90.12
89.45
89.78




MCRS consistently outperforms baseline models such as ResNet, ViT, I3D, BLIP, and DenseNet on multiple datasets (see Tables 1–2, p.13).



🧭 Future Work



Enhance cultural nuance modeling for more complex scenarios

Expand multilingual support to underrepresented languages

Integrate external tourism knowledge graphs for richer context

Optimize deployment for real-time evaluation in mobile environments




📜 License

This project is licensed under the MIT License.



🙏 Acknowledgments

This research was conducted at Guangxi Minzu University.Authors: Lin Gao (first author), Wenxuan Zhu (corresponding author).This work integrates multilingual NLP, role-playing simulation, and tourism evaluation to improve service adaptability and cross-cultural understanding.