  Semantic matching is of central significance to the answer selection task
which aims to select correct answers for a given question from a candidate
answer pool. A useful method is to employ neural networks with attention to
generate sentences representations in a way that information from pair
sentences can mutually influence the computation of representations. In this
work, an effective architecture,multi-size neural network with attention
mechanism (AM-MSNN),is introduced into the answer selection task. This
architecture captures more levels of language granularities in parallel,
because of the various sizes of filters comparing with single-layer CNN and
multi-layer CNNs. Meanwhile it extends the sentence representations by
attention mechanism, thus containing more information for different types of
questions. The empirical study on three various benchmark tasks of answer
selection demonstrates the efficacy of the proposed model in all the benchmarks
and its superiority over competitors. The experimental results show that (1)
multi-size neural network (MSNN) is a more useful method to capture abstract
features on different levels of granularities than single/multi-layer CNNs; (2)
the attention mechanism (AM) is a better strategy to derive more informative
representations; (3) AM-MSNN is a better architecture for the answer selection
task for the moment.
