  Many questions cannot be answered simply; their answers must include numerous
nuanced details and additional context. Complex Answer Retrieval (CAR) is the
retrieval of answers to such questions. In their simplest form, these questions
are constructed from a topic entity (e.g., `cheese') and a facet (e.g., `health
effects'). While topic matching has been thoroughly explored, we observe that
some facets use general language that is unlikely to appear verbatim in
answers. We call these low-utility facets. In this work, we present an approach
to CAR that identifies and addresses low-utility facets. We propose two
estimators of facet utility. These include exploiting the hierarchical
structure of CAR queries and using facet frequency information from training
data. To improve the retrieval performance on low-utility headings, we also
include entity similarity scores using knowledge graph embeddings. We apply our
approaches to a leading neural ranking technique, and evaluate using the TREC
CAR dataset. We find that our approach perform significantly better than the
unmodified neural ranker and other leading CAR techniques. We also provide a
detailed analysis of our results, and verify that low-utility facets are indeed
more difficult to match, and that our approach improves the performance for
these difficult queries.
