  Recently, pre-trained models have been the dominant paradigm in natural
language processing. They achieved remarkable state-of-the-art performance
across a wide range of related tasks, such as textual entailment, natural
language inference, question answering, etc. BERT, proposed by Devlin et.al.,
has achieved a better marked result in GLUE leaderboard with a deep transformer
architecture. Despite its soaring popularity, however, BERT has not yet been
applied to answer selection. This task is different from others with a few
nuances: first, modeling the relevance and correctness of candidates matters
compared to semantic relatedness and syntactic structure; second, the length of
an answer may be different from other candidates and questions. In this paper.
we are the first to explore the performance of fine-tuning BERT for answer
selection. We achieved STOA results across five popular datasets, demonstrating
the success of pre-trained models in this task.
