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