检测歧义以指导查询重写，实现企业级AI助手中的鲁棒对话
Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in Enterprise AI Assistants
AI总结：
本文提出“歧义引导的查询重写”框架，结合基于真实日志构建的分类器与查询重写模块，有效检测并消解企业AI助手多轮对话中的歧义，已部署于Adobe Experience Platform AI Assistant。
摘要：
与企业级AI助手进行多轮对话可能具有挑战性，因为问题中存在对话依赖关系，从而导致歧义和错误。为解决这一问题，我们提出了一种NLU-NLG框架，通过自动重构查询来实现歧义检测与消解，并引入了一项名为“歧义引导的查询重写”（Ambiguity-guided Query Rewrite）的新任务。为检测歧义，我们基于真实用户对话日志构建了一套分类体系，并从中汲取洞见来设计规则和提取特征，用于训练一个分类器。该分类器在检测歧义查询方面表现出优越的性能，优于基于LLM的基线方法。此外，将查询重写模块与我们的歧义检测分类器耦合后表明，这一端到端框架能够有效缓解歧义，同时避免对清晰查询进行不必要的多余短语插入，从而提升了AI助手的整体性能。鉴于其重要性，该系统已部署在实际应用中，即Adobe Experience Platform AI Assistant。
英文摘要：
Multi-turn conversations with an Enterprise AI Assistant can be challenging due to conversational dependencies in questions, leading to ambiguities and errors. To address this, we propose an NLU-NLG framework for ambiguity detection and resolution through reformulating query automatically and introduce a new task called "Ambiguity-guided Query Rewrite." To detect ambiguities, we develop a taxonomy based on real user conversational logs and draw insights from it to design rules and extract features for a classifier which yields superior performance in detecting ambiguous queries, outperforming LLM-based baselines. Furthermore, coupling the query rewrite module with our ambiguity detecting classifier shows that this end-to-end framework can effectively mitigate ambiguities without risking unnecessary insertions of unwanted phrases for clear queries, leading to an improvement in the overall performance of the AI Assistant. Due to its significance, this has been deployed in the real world application, namely Adobe Experience Platform AI Assistant.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
