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@article{163264,
        author = {Rachana Parikh and Dr. Hiren Joshi},
        title = {End-to-End Gujarati Task-Oriented Dialogue Management using Reinforcement Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2904-2911},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163264},
        abstract = {Nowadays, there's an increased demand for dialogue systems in local languages due to the ongoing need for continuous support in specific service domains. Ra-ther than relying solely on human resources, dialogue systems offer a viable solu-tion. Dialogue management plays a pivotal role in determining the most effective actions for the system at each stage. In this study, we introduce a task-oriented dialogue system for Gujarati language, leveraging reinforcement learning. This system comprises three key components: natural language understanding (NLU), Dialogue Management (DM), and Natural Language Generation (NLG). Our model seamlessly interacts with databases, extracting valuable information. Rein-forcement learning is employed specifically for the DM, employing an enhanced Deep Q-learning Network (DQN) strategy to bolster the agent's resilience against environmental noise. Additionally, we propose a unified model for the NLU module, demonstrating its effectiveness through experiments conducted on Guja-rati dialogue datasets. The results showcase the superior performance of our model over the conventional rule-based multi-turn dialogue system for Gujarati dialogues.},
        keywords = {Task-Oriented Dialogue, Reinforcement Learning, Deep Q-learning (DQN), End-to-End dialogue management model},
        month = {},
        }