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The bidirectional encoder representations from transformers (BERT) model hasrecently advanced the state-of-the-art in passage re-ranking. In this paper, weanalyze the results produced by a fine-tuned BERT model to better understandthe reasons behind such substantial improvements. To this aim, we focus on theMS MARCO passage re-ranking dataset and provide potential reasons for thesuccesses and failures of BERT for retrieval. In more detail, we empiricallystudy a set of hypotheses and provide additional analysis to explain thesuccessful performance of BERT.

Investigating the Successes and Failures of BERT for Passage Re-Ranking