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Neural networks provide new possibilities to automatically learn complexlanguage patterns and query-document relations. Neural IR models have achievedpromising results in learning query-document relevance patterns, but fewexplorations have been done on understanding the text content of a query or adocument. This paper studies leveraging a recently-proposed contextual neurallanguage model, BERT, to provide deeper text understanding for IR. Experimentalresults demonstrate that the contextual text representations from BERT are moreeffective than traditional word embeddings. Compared to bag-of-words retrievalmodels, the contextual language model can better leverage language structures,bringing large improvements on queries written in natural languages. Combiningthe text understanding ability with search knowledge leads to an enhancedpre-trained BERT model that can benefit related search tasks where trainingdata are limited.

Deeper Text Understanding for IR with Contextual Neural Language Modeling