Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis
This work addresses a specific problem in ABSA for natural language processing applications, representing an incremental improvement over existing pre-trained language models.
The paper tackled the challenge of incorporating dynamic semantic changes into aspect-based sentiment analysis (ABSA) by proposing DR-BERT, a method that uses a Dynamic Re-weighting Adapter to focus on important words for aspect-aware sentiment understanding, with experimental results on three benchmark datasets demonstrating its effectiveness.
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.