自动检测企业对话中的模糊问题并重写，提升AI助手对话鲁棒性。
Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in Enterprise AI Assistants
- 基于真实对话日志构建模糊性分类体系，设计规则与特征用于检测。
- 相比大模型基线，检测准确率更高，且避免对清晰问题的误改。
- 已落地Adobe Experience Platform AI助手，显著提升实际对话效果。
企业级AI助手在多轮对话中常因问题间的语义依赖导致模糊和错误。为此，我们提出一种NLU-NLG框架，通过自动重写查询来检测与解决模糊性，并引入新任务“模糊性引导的查询重写”。为检测模糊性，我们基于真实用户对话日志构建分类体系，从中提炼规则并提取特征，训练出性能优于大模型基线的分类器。进一步将查询重写模块与模糊检测分类器结合，形成端到端框架，可在不增加清晰问题冗余内容的前提下有效缓解模糊性，整体提升AI助手表现。该方法已在Adobe Experience Platform AI助手中实际部署。
原文摘要 · Abstract (English)
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.
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