  Answer selection (AS) is an essential subtask in the field of natural
language processing with an objective to identify the most likely answer to a
given question from a corpus containing candidate answer sentences. A common
approach to address the AS problem is to generate an embedding for each
candidate sentence and query. Then, select the sentence whose vector
representation is closest to the query's. A key drawback is the low quality of
the embeddings, hitherto, based on its performance on AS benchmark datasets. In
this work, we present ASBERT, a framework built on the BERT architecture that
employs Siamese and Triplet neural networks to learn an encoding function that
maps a text to a fixed-size vector in an embedded space. The notion of distance
between two points in this space connotes similarity in meaning between two
texts. Experimental results on the WikiQA and TrecQA datasets demonstrate that
our proposed approach outperforms many state-of-the-art baseline methods.
