  In this paper, we propose a novel method for a sentence-level
answer-selection task that is a fundamental problem in natural language
processing. First, we explore the effect of additional information by adopting
a pretrained language model to compute the vector representation of the input
text and by applying transfer learning from a large-scale corpus. Second, we
enhance the compare-aggregate model by proposing a novel latent clustering
method to compute additional information within the target corpus and by
changing the objective function from listwise to pointwise. To evaluate the
performance of the proposed approaches, experiments are performed with the
WikiQA and TREC-QA datasets. The empirical results demonstrate the superiority
of our proposed approach, which achieve state-of-the-art performance for both
datasets.
