  When re-finding items, users who forget or are uncertain about identifying
details often rely on creative strategies for expressing their information
needs -- complex queries that describe content elements (e.g., book characters
or events), information beyond the document text (e.g., descriptions of book
covers), or personal context (e.g., when they read a book). This retrieval
setting, called tip of the tongue (TOT), is especially challenging for models
heavily reliant on lexical and semantic overlap between query and document
text. In this work, we introduce a simple yet effective framework for handling
such complex queries by decomposing the query into individual clues, routing
those as sub-queries to specialized retrievers, and ensembling the results.
This approach allows us to take advantage of off-the-shelf retrievers (e.g.,
CLIP for retrieving images of book covers) or incorporate retriever-specific
logic (e.g., date constraints). We show that our framework incorportating query
decompositions into retrievers can improve gold book recall up to 7% relative
again for Recall@5 on a new collection of 14,441 real-world query-book pairs
from an online community for resolving TOT inquiries.
