Aspect level sentiment classification is a fine-grained sentiment analysis
task. To detect the sentiment towards a particular aspect in a sentence,
previous studies have developed various attention-based methods for generating
aspect-specific sentence representations. However, the attention may inherently
introduce noise and downgrade the performance. In this paper, we propose
constrained attention networks (CAN), a simple yet effective solution, to
regularize the attention for multi-aspect sentiment analysis, which alleviates
the drawback of the attention mechanism. Specifically, we introduce orthogonal
regularization on multiple aspects and sparse regularization on each single
aspect. Experimental results on two public datasets demonstrate the
effectiveness of our approach. We further extend our approach to multi-task
settings and outperform the state-of-the-art methods.

本文提出了约束注意力网络，它是一个简单而有效的解决方案，用于规范多方面情感分析中的注意力，从而缓解了注意机制的缺点，并在两个公共数据集上取得了很好的实验结果，并在多任务设置中超越了最先进的方法。