Aspect-based Sentiment Analysis through EDU-level Attentions
This work addresses a specific challenge in natural language processing for sentiment analysis, offering an incremental improvement over existing methods.
The paper tackles the problem of aspect-based sentiment analysis in sentences with multiple aspects and conflicting sentiments by modeling elementary discourse units (EDUs) with word- and EDU-level attentions, achieving state-of-the-art performance on three benchmark datasets.
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.