  In this paper, we present a novel approach for incorporating external
knowledge in Recurrent Neural Networks (RNNs). We propose the integration of
lexicon features into the self-attention mechanism of RNN-based architectures.
This form of conditioning on the attention distribution, enforces the
contribution of the most salient words for the task at hand. We introduce three
methods, namely attentional concatenation, feature-based gating and affine
transformation. Experiments on six benchmark datasets show the effectiveness of
our methods. Attentional feature-based gating yields consistent performance
improvement across tasks. Our approach is implemented as a simple add-on module
for RNN-based models with minimal computational overhead and can be adapted to
any deep neural architecture.
