Query intent classification, which aims at assisting customers to find desired products, has become an essential component of the e-commerce search. Existing query intent classification models either design more exquisite models to enhance the representation learning of queries or explore label-graph and multi-task to facilitate models to learn external information. However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories. This paper proposes a Multi-granularity Matching Attention Network (MMAN), which contains three modules: a self-matching module, a char-level matching module, and a semantic-level matching module to comprehensively extract features from the query and a query-category interaction matrix. In this way, the model can eliminate the difference in expression between queries and categories for query intent classification. We conduct extensive offline and online A/B experiments, and the results show that the MMAN significantly outperforms the strong baselines, which shows the superiority and effectiveness of MMAN. MMAN has been deployed in production and brings great commercial value for our company.

翻译：查询意图分类旨在帮助顾客找到所需商品，已成为电商搜索的核心组成部分。现有查询意图分类模型要么设计更精细的模型以增强查询的表示学习，要么探索标签图与多任务学习以帮助模型获取外部信息。然而，这些模型无法从查询与类别中捕获多粒度匹配特征，导致其难以弥合非正式查询与类别之间表达的差异。本文提出一种多粒度匹配注意力网络（MMAN），该网络包含三个模块：自匹配模块、字符级匹配模块和语义级匹配模块，用于从查询及查询-类别交互矩阵中全面提取特征。通过这种方式，模型能够消除查询意图分类中查询与类别之间的表达差异。我们进行了广泛的离线与在线A/B实验，结果表明MMAN显著优于各类强基线模型，验证了其优越性与有效性。MMAN已部署于生产环境，为公司带来了巨大的商业价值。