  Although deep neural networks generally have fixed network structures, the
concept of dynamic mechanism has drawn more and more attention in recent years.
Attention mechanisms compute input-dependent dynamic attention weights for
aggregating a sequence of hidden states. Dynamic network configuration in
convolutional neural networks (CNNs) selectively activates only part of the
network at a time for different inputs. In this paper, we combine the two
dynamic mechanisms for text classification tasks. Traditional attention
mechanisms attend to the whole sequence of hidden states for an input sentence,
while in most cases not all attention is needed especially for long sequences.
We propose a novel method called Gated Attention Network (GA-Net) to
dynamically select a subset of elements to attend to using an auxiliary
network, and compute attention weights to aggregate the selected elements. It
avoids a significant amount of unnecessary computation on unattended elements,
and allows the model to pay attention to important parts of the sequence.
Experiments in various datasets show that the proposed method achieves better
performance compared with all baseline models with global or local attention
while requiring less computation and achieving better interpretability. It is
also promising to extend the idea to more complex attention-based models, such
as transformers and seq-to-seq models.
