Currently, attention mechanism becomes a standard fixture in most
state-of-the-art natural language processing (NLP) models, not only due to
outstanding performance it could gain, but also due to plausible innate
explanation for the behaviors of neural architectures it provides, which is
notoriously difficult to analyze. However, recent studies show that attention
is unstable against randomness and perturbations during training or testing,
such as random seeds and slight perturbation of embedding vectors, which
impedes it from becoming a faithful explanation tool. Thus, a natural question
is whether we can find some substitute of the current attention which is more
stable and could keep the most important characteristics on explanation and
prediction of attention. In this paper, to resolve the problem, we provide a
first rigorous definition of such alternate namely SEAT (Stable and Explainable
Attention). Specifically, a SEAT should has the following three properties: (1)
Its prediction distribution is enforced to be close to the distribution based
on the vanilla attention; (2) Its top-k indices have large overlaps with those
of the vanilla attention; (3) It is robust w.r.t perturbations, i.e., any
slight perturbation on SEAT will not change the prediction distribution too
much, which implicitly indicates that it is stable to randomness and
perturbations. Finally, through intensive experiments on various datasets, we
compare our SEAT with other baseline methods using RNN, BiLSTM and BERT
architectures via six different evaluation metrics for model interpretation,
stability and accuracy. Results show that SEAT is more stable against different
perturbations and randomness while also keeps the explainability of attention,
which indicates it is a more faithful explanation. Moreover, compared with
vanilla attention, there is almost no utility (accuracy) degradation for SEAT.

本文提出 SEAT，即稳定且可解释的注意力机制。通过与其他基线方法的比较，研究者发现 SEAT 可以更好地保持注意力的可解释性和稳定性，同时保持较高的准确性。