Related papers: Seq2Seq Model-Based Chatbot with LSTM and Attention Mechanism for Enhanced User Interaction

Seq2Seq Model-Based Chatbot with LSTM and Attention Mechanism for Enhanced User Interaction

URL: http://arxiv.org/abs/2501.00049v1
Date: Fri, 27 Dec 2024 23:50:54 GMT
Title: Seq2Seq Model-Based Chatbot with LSTM and Attention Mechanism for Enhanced User Interaction
Authors: Lamya Benaddi, Charaf Ouaddi, Adnane Souha, Abdeslam Jakimi, Mohamed Rahouti, Mohammed Aledhari, Diogo Oliveira, Brahim Ouchao,
Abstract summary: This work proposes a Sequence-to-Sequence (Seq2Seq) model with an encoder-decoder architecture that incorporates attention mechanisms and Long Short-Term Memory (LSTM) cells. The proposed Seq2Seq model-based robot is trained, validated, and tested on a dataset specifically for the tourism sector in Draa-Tafilalet, Morocco.
Score: 1.937324318931008
License:
Abstract: A chatbot is an intelligent software application that automates conversations and engages users in natural language through messaging platforms. Leveraging artificial intelligence (AI), chatbots serve various functions, including customer service, information gathering, and casual conversation. Existing virtual assistant chatbots, such as ChatGPT and Gemini, demonstrate the potential of AI in Natural Language Processing (NLP). However, many current solutions rely on predefined APIs, which can result in vendor lock-in and high costs. To address these challenges, this work proposes a chatbot developed using a Sequence-to-Sequence (Seq2Seq) model with an encoder-decoder architecture that incorporates attention mechanisms and Long Short-Term Memory (LSTM) cells. By avoiding predefined APIs, this approach ensures flexibility and cost-effectiveness. The chatbot is trained, validated, and tested on a dataset specifically curated for the tourism sector in Draa-Tafilalet, Morocco. Key evaluation findings indicate that the proposed Seq2Seq model-based chatbot achieved high accuracies: approximately 99.58% in training, 98.03% in validation, and 94.12% in testing. These results demonstrate the chatbot's effectiveness in providing relevant and coherent responses within the tourism domain, highlighting the potential of specialized AI applications to enhance user experience and satisfaction in niche markets.

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