  In aspect-based sentiment analysis (ABSA), many neural models are equipped
with an attention mechanism to quantify the contribution of each context word
to sentiment prediction. However, such a mechanism suffers from one drawback:
only a few frequent words with sentiment polarities are tended to be taken into
consideration for final sentiment decision while abundant infrequent sentiment
words are ignored by models. To deal with this issue, we propose a progressive
self-supervised attention learning approach for attentional ABSA models. In
this approach, we iteratively perform sentiment prediction on all training
instances, and continually learn useful attention supervision information in
the meantime. During training, at each iteration, context words with the
highest impact on sentiment prediction, identified based on their attention
weights or gradients, are extracted as words with active/misleading influence
on the correct/incorrect prediction for each instance. Words extracted in this
way are masked for subsequent iterations. To exploit these extracted words for
refining ABSA models, we augment the conventional training objective with a
regularization term that encourages ABSA models to not only take full advantage
of the extracted active context words but also decrease the weights of those
misleading words. We integrate the proposed approach into three
state-of-the-art neural ABSA models. Experiment results and in-depth analyses
show that our approach yields better attention results and significantly
enhances the performance of all three models. We release the source code and
trained models at https://github.com/DeepLearnXMU/PSSAttention.
