  A sentence may express sentiments on multiple aspects. When these aspects are
associated with different sentiment polarities, a model's accuracy is often
adversely affected. We observe that multiple aspects in such hard sentences are
mostly expressed through multiple clauses, or formally known as elementary
discourse units (EDUs), and one EDU tends to express a single aspect with
unitary sentiment towards that aspect. In this paper, we propose to consider
EDU boundaries in sentence modeling, with attentions at both word and EDU
levels. Specifically, we highlight sentiment-bearing words in EDU through
word-level sparse attention. Then at EDU level, we force the model to attend to
the right EDU for the right aspect, by using EDU-level sparse attention and
orthogonal regularization. Experiments on three benchmark datasets show that
our simple EDU-Attention model outperforms state-of-the-art baselines. Because
EDU can be automatically segmented with high accuracy, our model can be applied
to sentences directly without the need of manual EDU boundary annotation.
