  Answer selection is an important research problem, with applications in many
areas. Previous deep learning based approaches for the task mainly adopt the
Compare-Aggregate architecture that performs word-level comparison followed by
aggregation. In this work, we take a departure from the popular
Compare-Aggregate architecture, and instead, propose a new gated self-attention
memory network for the task. Combined with a simple transfer learning technique
from a large-scale online corpus, our model outperforms previous methods by a
large margin, achieving new state-of-the-art results on two standard answer
selection datasets: TrecQA and WikiQA.
