Nasri-Lowshani, M. J., Salimi Sartakhti, J., Ebrahimpour-Komole, H.. (1404). Deep Reinforcement Learning for Efficient Multilingual Dialogue Management. فناوری آموزش, (), 11-22. doi: 10.22061/jecei.2025.11348.814
M. J. Nasri-Lowshani; J. Salimi Sartakhti; H. Ebrahimpour-Komole. "Deep Reinforcement Learning for Efficient Multilingual Dialogue Management". فناوری آموزش, , , 1404, 11-22. doi: 10.22061/jecei.2025.11348.814
Nasri-Lowshani, M. J., Salimi Sartakhti, J., Ebrahimpour-Komole, H.. (1404). 'Deep Reinforcement Learning for Efficient Multilingual Dialogue Management', فناوری آموزش, (), pp. 11-22. doi: 10.22061/jecei.2025.11348.814
Nasri-Lowshani, M. J., Salimi Sartakhti, J., Ebrahimpour-Komole, H.. Deep Reinforcement Learning for Efficient Multilingual Dialogue Management. فناوری آموزش, 1404; (): 11-22. doi: 10.22061/jecei.2025.11348.814
Deep Reinforcement Learning for Efficient Multilingual Dialogue Management
Journal of Electrical and Computer Engineering Innovations (JECEI)
Department of Artificial Intelligence, Faculty of Electrical and Computer Engineering, University of Kashan, Kashan, Iran.
تاریخ دریافت: 28 دی 1403،
تاریخ بازنگری: 06 فروردین 1404،
تاریخ پذیرش: 19 فروردین 1404
چکیده
Background and Objectives: Developing efficient task-oriented dialogue systems capable of handling multilingual interactions is a growing area of research in natural language processing (NLP). In this paper, we propose SenSimpleDS, a deep reinforcement learning-based joint task-oriented dialogue system, designed for multilingual conversations. Methods: The system utilizes a deep Q-network and the SBERT model to represent the dialogue environment. We introduce two variants, SenSimpleDS+ and SenSimpleDS-NSP, which incorporate modifications in the ε-greedy method and leverage next sequence prediction (NSP) using BERT to refine the reward function. These methods are evaluated on datasets in English, Persian, Spanish, and German, and compared with baseline methods such as SimpleDS and SCGSimpleDS. Results: Our experimental results demonstrate that the proposed methods outperform the baselines in terms of average collected rewards, requiring fewer learning steps to achieve optimal dialogue policies. Notably, the incorporation of NSP significantly improves performance by optimizing reward collection. The multilingual SenSimpleDS further showcases the system’s ability to function across languages using a random forest classifier for language detection and MPNet for environment construction. In addition to system evaluations, we introduce a new Persian dataset for task-oriented dialogue in the restaurant domain, expanding the resources available for developing dialogue systems in low-resource languages. Conclusion: SenSimpleDS, a deep reinforcement learning-based joint task-oriented dialogue system, demonstrates superior performance over baseline methods by leveraging deep Q-networks, SBERT. The integration of next sequence prediction (NSP) significantly enhances reward optimization, enabling faster convergence to optimal dialogue policies. This work establishes a foundation for future research in multilingual dialogue systems, with potential applications across diverse service domains.
