  Attention mechanism has been used as an ancillary means to help RNN or CNN.
However, the Transformer (Vaswani et al., 2017) recently recorded the
state-of-the-art performance in machine translation with a dramatic reduction
in training time by solely using attention. Motivated by the Transformer,
Directional Self Attention Network (Shen et al., 2017), a fully attention-based
sentence encoder, was proposed. It showed good performance with various data by
using forward and backward directional information in a sentence. But in their
study, not considered at all was the distance between words, an important
feature when learning the local dependency to help understand the context of
input text. We propose Distance-based Self-Attention Network, which considers
the word distance by using a simple distance mask in order to model the local
dependency without losing the ability of modeling global dependency which
attention has inherent. Our model shows good performance with NLI data, and it
records the new state-of-the-art result with SNLI data. Additionally, we show
that our model has a strength in long sentences or documents.
