Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a
specific aspect in the given sentence. While pre-trained language models such
as BERT have achieved great success, incorporating dynamic semantic changes
into ABSA remains challenging. To this end, in this paper, we propose to
address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method
designed to learn dynamic aspect-oriented semantics for ABSA. Specifically, we
first take the Stack-BERT layers as a primary encoder to grasp the overall
semantic of the sentence and then fine-tune it by incorporating a lightweight
Dynamic Re-weighting Adapter (DRA). Note that the DRA can pay close attention
to a small region of the sentences at each step and re-weigh the vitally
important words for better aspect-aware sentiment understanding. Finally,
experimental results on three benchmark datasets demonstrate the effectiveness
and the rationality of our proposed model and provide good interpretable
insights for future semantic modeling.

本文提出了一种利用 Dynamic Re-weighting BERT 来学习 aspect-oriented semantics 从而预测句子中特定方面的情感极性的方法，实验结果表明该模型对于未来的语义建模具有可解释性的洞见。