  Document retrieval has greatly benefited from the advancements of large-scale
pre-trained language models (PLMs). However, their effectiveness is often
limited in theme-specific applications for specialized areas or industries, due
to unique terminologies, incomplete contexts of user queries, and specialized
search intents. To capture the theme-specific information and improve
retrieval, we propose to use a corpus topical taxonomy, which outlines the
latent topic structure of the corpus while reflecting user-interested aspects.
We introduce ToTER (Topical Taxonomy Enhanced Retrieval) framework, which
identifies the central topics of queries and documents with the guidance of the
taxonomy, and exploits their topical relatedness to supplement missing
contexts. As a plug-and-play framework, ToTER can be flexibly employed to
enhance various PLM-based retrievers. Through extensive quantitative, ablative,
and exploratory experiments on two real-world datasets, we ascertain the
benefits of using topical taxonomy for retrieval in theme-specific applications
and demonstrate the effectiveness of ToTER.
