  We study the use of BERT for non-factoid question-answering, focusing on the
passage re-ranking task under varying passage lengths. To this end, we explore
the fine-tuning of BERT in different learning-to-rank setups, comprising both
point-wise and pair-wise methods, resulting in substantial improvements over
the state-of-the-art. We then analyze the effectiveness of BERT for different
passage lengths and suggest how to cope with large passages.
