标题： 基于LSTM和注意力机制的Seq2Seq模型聊天机器人以增强用户体验 显示英文标题

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

摘要： 聊天机器人是一种智能软件应用程序，它通过消息平台以自然语言自动进行对话并与用户互动。 利用人工智能（AI），聊天机器人可以执行各种功能，包括客户服务、信息收集和随意对话。 现有的虚拟助手聊天机器人，如ChatGPT和Gemini，展示了人工智能在自然语言处理（NLP）方面的潜力。 然而，许多当前的解决方案依赖于预定义的API，这可能导致供应商锁定和高昂的成本。 为了解决这些挑战，本研究提出了一种使用基于序列到序列（Seq2Seq）模型并采用编码器-解码器架构的聊天机器人，该架构结合了注意力机制和长短期记忆（LSTM）单元。 通过避免使用预定义的API，这种方法确保了灵活性和成本效益。 该聊天机器人在一个专门为摩洛哥德拉Tafilalet地区的旅游业定制的数据集上进行了训练、验证和测试。 关键评估结果显示，所提出的基于Seq2Seq模型的聊天机器人在训练中达到了约99.58%的准确率，在验证中达到98.03%，在测试中达到94.12%。 这些结果表明，该聊天机器人在旅游领域内提供相关且连贯的响应方面非常有效，突显了专门化的AI应用在利基市场中提升用户体验和满意度的潜力。 显示英文摘要

摘要： 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.