Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
This addresses interpretability and noise issues in sentiment analysis for NLP applications, though it is an incremental improvement.
The paper tackles document-level aspect sentiment classification by proposing a hierarchical reinforcement learning approach that selects relevant clauses and words to reduce data noise, achieving state-of-the-art results.
Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC. This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC. First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses. Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses. Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection. Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.