An Aspect Sentiment Triplet Extraction Method based on Syntax-Guided Muti-Turn Machine Reading Comprehension

The Aspect Sentiment Triplet Extraction (ASTE) task, which has the goal of extracting sentiment triplets from sentences, has been solved by the Machine Reading Comprehension (MRC) framework. Although the MRC model can effectively handle the ASTE task, there still remain issues. Traditional attention models do not explicitly prioritize and concentrate on the significance of words, which can result in the incorrect emphasis on less crucial words and assign higher attention weights to less important terms. Therefore, in this paper, a syntactically guided model is proposed. It incorporates syntax relationships from input to constrain the attention mechanism. We developed a model called Syntax-Guided Muti-Turn Machine Reading Comprehension (SG-MTMRC), which incorporates syntax relationships into the layer of self-attention by leveraging a Syntax-Guided Network (SG-NET). It creates a syntax-guided self-attention layer to enhance the input representation. Then, we put them into a Muti-Turn Machine Reading Comprehension (MTMRC) model for further processing. Four benchmark datasets are being used for extensive experiments so as to validate our approach's effectiveness. The experiment results indicate significant performance improvement achieved by the SG-MTMRC model.