Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions. When there is an entity error, it imposes extra challenges for a dialogue system to produce satisfactory responses. In this work, we propose KG-ECO: Knowledge Graph enhanced Entity COrrection for query rewriting, an entity correction system with corrupt entity span detection and entity retrieval/re-ranking functionalities.To boost the model performance, we incorporate Knowledge Graph (KG) to provide entity structural information (neighboring entities encoded by graph neural networks) and textual information (KG entity descriptions encoded by RoBERTa). Experimental results show that our approach yields a clear performance gain over two baselines: utterance level QR and entity correction without utilizing KG information. The proposed system is particularly effective for few-shot learning cases where target entities are rarely seen in training or there is a KG relation between the target entity and other contextual entities in the query.

翻译：查询重写在大型对话系统中扮演着关键角色，旨在减少交互摩擦。当存在实体错误时，对话系统生成满意响应的难度将显著增加。本文提出KG-ECO：基于知识图谱增强实体纠正的查询重写系统，该系统具备错误实体跨度检测与实体检索/重排序功能。为提升模型性能，我们引入知识图谱提供实体结构信息（由图神经网络编码的相邻实体）与文本信息（由RoBERTa编码的知识图谱实体描述）。实验结果表明，相较于两种基线方法（话语级查询重写与未利用知识图谱信息的实体纠正），本方法取得了显著性能提升。所提系统在少样本学习场景中尤为有效，例如目标实体在训练集中极少出现，或目标实体与查询中其他上下文实体存在知识图谱关联时。