  Deep neural networks have recently shown promise in the ad-hoc retrieval
task. However, such models have often been based on one field of the document,
for example considering document title only or document body only. Since in
practice documents typically have multiple fields, and given that non-neural
ranking models such as BM25F have been developed to take advantage of document
structure, this paper investigates how neural models can deal with multiple
document fields. We introduce a model that can consume short text fields such
as document title and long text fields such as document body. It can also
handle multi-instance fields with variable number of instances, for example
where each document has zero or more instances of incoming anchor text. Since
fields vary in coverage and quality, we introduce a masking method to handle
missing field instances, as well as a field-level dropout method to avoid
relying too much on any one field. As in the studies of non-neural field
weighting, we find it is better for the ranker to score the whole document
jointly, rather than generate a per-field score and aggregate. We find that
different document fields may match different aspects of the query and
therefore benefit from comparing with separate representations of the query
text. The combination of techniques introduced here leads to a neural ranker
that can take advantage of full document structure, including multiple instance
and missing instance data, of variable length. The techniques significantly
enhance the performance of the ranker, and also outperform a learning to rank
baseline with hand-crafted features.
