混合方法提升波斯语对话状态追踪准确率与连贯性
Hybrid Dialogue State Tracking for Persian Chatbots: A Language Model-Based Approach
- 结合规则与语言模型，用BERT填槽、XGBoost验证意图、GPT生成状态
- 在波斯语多轮对话数据集上准确率显著优于现有方法
- 适合需要高适应性与自然对话体验的波斯语聊天机器人开发
对话状态追踪（DST）是对话式AI的关键组件，旨在深入理解对话上下文并引导对话以响应用户请求。由于开放域和多轮对话系统需求日益增长，传统基于规则的DST方法效率不足，难以在复杂对话中提供类人交互所需的适应性和连贯性。本文提出一种混合式DST模型，融合规则方法与语言模型：使用BERT进行槽位填充与意图识别，XGBoost用于意图验证，GPT实现对话状态追踪，同时通过在线代理实现实时回答生成。该模型专为波斯语多轮对话数据集设计并评估，结果表明其在准确率和连贯性上显著优于现有方法，充分展现了混合策略在提升对话状态追踪能力方面的有效性，为更定制化、自适应且类人的对话系统奠定基础。
原文摘要 · Abstract (English)
Dialogue State Tracking (DST) is an essential element of conversational AI with the objective of deeply understanding the conversation context and leading it toward answering user requests. Due to high demands for open-domain and multi-turn chatbots, the traditional rule-based DST is not efficient enough, since it cannot provide the required adaptability and coherence for human-like experiences in complex conversations. This study proposes a hybrid DST model that utilizes rule-based methods along with language models, including BERT for slot filling and intent detection, XGBoost for intent validation, GPT for DST, and online agents for real-time answer generation. This model is uniquely designed to be evaluated on a comprehensive Persian multi-turn dialogue dataset and demonstrated significantly improved accuracy and coherence over existing methods in Persian-based chatbots. The results demonstrate how effectively a hybrid approach may improve DST capabilities, paving the way for conversational AI systems that are more customized, adaptable, and human-like.
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