  Answer selection (answer ranking) is one of the key steps in many kinds of
question answering (QA) applications, where deep models have achieved
state-of-the-art performance. Among these deep models, recurrent neural network
(RNN) based models are most popular, typically with better performance than
convolutional neural network (CNN) based models. Nevertheless, it is difficult
for RNN based models to capture the information about long-range dependency
among words in the sentences of questions and answers. In this paper, we
propose a new deep model, called gated group self-attention (GGSA), for answer
selection. GGSA is inspired by global self-attention which is originally
proposed for machine translation and has not been explored in answer selection.
GGSA tackles the problem of global self-attention that local and global
information cannot be well distinguished. Furthermore, an interaction mechanism
between questions and answers is also proposed to enhance GGSA by a residual
structure. Experimental results on two popular QA datasets show that GGSA can
outperform existing answer selection models to achieve state-of-the-art
performance. Furthermore, GGSA can also achieve higher accuracy than global
self-attention for the answer selection task, with a lower computation cost.
