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This paper describes Brown University's submission to the TREC 2019 DeepLearning track. We followed a 2-phase method for producing a ranking ofpassages for a given input query: In the the first phase, the user's query isexpanded by appending 3 queries generated by a transformer model which wastrained to rephrase an input query into semantically similar queries. Theexpanded query can exhibit greater similarity in surface form and vocabularyoverlap with the passages of interest and can therefore serve as enriched inputto any downstream information retrieval method. In the second phase, we use aBERT-based model pre-trained for language modeling but fine-tuned for query -document relevance prediction to compute relevance scores for a set of 1000candidate passages per query and subsequently obtain a ranking of passages bysorting them based on the predicted relevance scores. According to the resultspublished in the official Overview of the TREC Deep Learning Track 2019, ourteam ranked 3rd in the passage retrieval task (including full ranking andre-ranking), and 2nd when considering only re-ranking submissions.

Brown University at TREC Deep Learning 2019