  Neural networks provide new possibilities to automatically learn complex
language patterns and query-document relations. Neural IR models have achieved
promising results in learning query-document relevance patterns, but few
explorations have been done on understanding the text content of a query or a
document. This paper studies leveraging a recently-proposed contextual neural
language model, BERT, to provide deeper text understanding for IR. Experimental
results demonstrate that the contextual text representations from BERT are more
effective than traditional word embeddings. Compared to bag-of-words retrieval
models, the contextual language model can better leverage language structures,
bringing large improvements on queries written in natural languages. Combining
the text understanding ability with search knowledge leads to an enhanced
pre-trained BERT model that can benefit related search tasks where training
data are limited.
