Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e.g., products with aspects such as category and brand. Recent work has proposed two approaches to incorporate the aspect information into item representations for effective retrieval by predicting the values associated with the item aspects. Despite their efficacy, they treat the values as isolated classes (e.g., "Smart Homes", "Home, Garden & Tools", and "Beauty & Health") and ignore their fine-grained semantic relation. Furthermore, they either enforce the learning of aspects into the CLS token, which could confuse it from its designated use for representing the entire content semantics, or learn extra aspect embeddings only with the value prediction objective, which could be insufficient especially when there are no annotated values for an item aspect. Aware of these limitations, we propose a MUlti-granulaRity-aware Aspect Learning model (MURAL) for multi-aspect dense retrieval. It leverages aspect information across various granularities to capture both coarse and fine-grained semantic relations between values. Moreover, MURAL incorporates separate aspect embeddings as input to transformer encoders so that the masked language model objective can assist implicit aspect learning even without aspect-value annotations. Extensive experiments on two real-world datasets of products and mini-programs show that MURAL outperforms state-of-the-art baselines significantly.

翻译：稠密检索方法主要关注非结构化文本，而对具有多种方面的结构化数据（例如，具有类别和品牌等方面的产品）关注较少。近期研究提出了两种方法，通过预测与物品方面相关的值，将方面信息融入物品表示中，以实现有效检索。尽管这些方法有效，但它们将值视为孤立的类别（如“智能家居”、“家居、花园与工具”以及“美容与健康”），忽略了它们之间的细粒度语义关系。此外，它们要么将方面学习强制融入CLS令牌中，这可能使其偏离表示整个内容语义的指定用途；要么仅通过值预测目标学习额外的方面嵌入，这在物品方面没有标注值时可能不够充分。针对这些局限性，我们提出了一种面向多方面稠密检索的多粒度感知方面学习模型（MURAL）。该模型利用不同粒度下的方面信息，捕捉值之间的粗粒度和细粒度语义关系。此外，MURAL将独立的方面嵌入作为Transformer编码器的输入，使得掩码语言模型目标能够在没有方面-值标注的情况下辅助隐式方面学习。在两个真实世界数据集（产品和小程序）上的大量实验表明，MURAL显著优于最先进的基线方法。