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（QR）在大规模对话系统中对降低交互障碍具有关键作用。当存在实体错误时，对话系统生成满意响应的难度显著增加。本文提出KG-ECO：基于知识图谱增强实体纠错的查询重写方法，该系统具备错误实体跨度检测与实体检索/重排序功能。为了提升模型性能，我们引入知识图谱（KG）提供实体结构信息（通过图神经网络编码的邻接实体）和文本信息（通过RoBERTa编码的KG实体描述）。实验结果表明，相较于两种基线方法（话语级QR与未利用KG信息的实体纠错），本文方法取得了显著的性能提升。特别地，当目标实体在训练集中极少出现，或目标实体与查询中其它语境实体存在KG关联时，所提系统对小样本学习场景尤为有效。