  Classical information retrieval (IR) methods, such as query likelihood and
BM25, score documents independently w.r.t. each query term, and then accumulate
the scores. Assuming query term independence allows precomputing term-document
scores using these models---which can be combined with specialized data
structures, such as inverted index, for efficient retrieval. Deep neural IR
models, in contrast, compare the whole query to the document and are,
therefore, typically employed only for late stage re-ranking. We incorporate
query term independence assumption into three state-of-the-art neural IR
models: BERT, Duet, and CKNRM---and evaluate their performance on a passage
ranking task. Surprisingly, we observe no significant loss in result quality
for Duet and CKNRM---and a small degradation in the case of BERT. However, by
operating on each query term independently, these otherwise computationally
intensive models become amenable to offline precomputation---dramatically
reducing the cost of query evaluations employing state-of-the-art neural
ranking models. This strategy makes it practical to use deep models for
retrieval from large collections---and not restrict their usage to late stage
re-ranking.
