  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.
