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Learning-to-Rank with BERT in TF-Ranking

Shuguang Han
arXiv, arXiv (2020)


This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance. This approach is proved to be effective in a public MS MARCO benchmark [3]. Our submissions achieve the best performance for the passage re-ranking task as of March 30, 2020 [4], and the second best performance for the passage full-ranking task as of April 10, 2020 [5], demonstrating the effectiveness of combining ranking losses with BERT representations for document ranking.