  Grounded on pre-trained language models (PLMs), dense retrieval has been
studied extensively on plain text. In contrast, there has been little research
on retrieving data with multiple aspects using dense models. In the scenarios
such as product search, the aspect information plays an essential role in
relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A
common way of leveraging aspect information for multi-aspect retrieval is to
introduce an auxiliary classification objective, i.e., using item contents to
predict the annotated value IDs of item aspects. However, by learning the value
embeddings from scratch, this approach may not capture the various semantic
similarities between the values sufficiently. To address this limitation, we
leverage the aspect information as text strings rather than class IDs during
pre-training so that their semantic similarities can be naturally captured in
the PLMs. To facilitate effective retrieval with the aspect strings, we propose
mutual prediction objectives between the text of the item aspect and content.
In this way, our model makes more sufficient use of aspect information than
conducting undifferentiated masked language modeling (MLM) on the concatenated
text of aspects and content. Extensive experiments on two real-world datasets
(product and mini-program search) show that our approach can outperform
competitive baselines both treating aspect values as classes and conducting the
same MLM for aspect and content strings. Code and related dataset will be
available at the URL \footnote{https://github.com/sunxiaojie99/ATTEMPT}.
