  Recent works using artificial neural networks based on distributed word
representation greatly boost performance on various natural language processing
tasks, especially the answer selection problem. Nevertheless, most of the
previous works used deep learning methods (like LSTM-RNN, CNN, etc.) only to
capture semantic representation of each sentence separately, without
considering the interdependence between each other. In this paper, we propose a
novel end-to-end learning framework which constitutes deep convolutional neural
network based on multi-modal similarity metric learning (M$^2$S-Net) on
pairwise tokens. The proposed model demonstrates its performance by surpassing
previous state-of-the-art systems on the answer selection benchmark, i.e.,
TREC-QA dataset, in both MAP and MRR metrics.
