  Complex answer retrieval (CAR) is the process of retrieving answers to
questions that have multifaceted or nuanced answers. In this work, we present
two novel approaches for CAR based on the observation that question facets can
vary in utility: from structural (facets that can apply to many similar topics,
such as 'History') to topical (facets that are specific to the question's
topic, such as the 'Westward expansion' of the United States). We first explore
a way to incorporate facet utility into ranking models during query term score
combination. We then explore a general approach to reform the structure of
ranking models to aid in learning of facet utility in the query-document term
matching phase. When we use our techniques with a leading neural ranker on the
TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and
yield up to 26% higher performance than the next best method.
