  Most neural Information Retrieval (Neu-IR) models derive query-to-document
ranking scores based on term-level matching. Inspired by TileBars, a classical
term distribution visualization method, in this paper, we propose a novel
Neu-IR model that handles query-to-document matching at the subtopic and higher
levels. Our system first splits the documents into topical segments,
"visualizes" the matchings between the query and the segments, and then feeds
an interaction matrix into a Neu-IR model, DeepTileBars, to obtain the final
ranking scores. DeepTileBars models the relevance signals occurring at
different granularities in a document's topic hierarchy. It better captures the
discourse structure of a document and thus the matching patterns. Although its
design and implementation are light-weight, DeepTileBars outperforms other
state-of-the-art Neu-IR models on benchmark datasets including the Text
REtrieval Conference (TREC) 2010-2012 Web Tracks and LETOR 4.0.
