Pair-wise aspect and opinion terms extraction as graph parsing via a novel mutually-aware interaction mechanism

Abstract

The pair-wise aspect and opinion term extraction (PAOTE) task aims to extract aspect terms and opinion terms from reviews in the form of opinion pairs, which provides a global profile for reviews of goods or users. Up-to-date studies ignore the interaction between term detection and term pairing, which may be crucial for the PAOTE task. Other studies use syntactic dependency structures to enhance their models, which cannot better provide task-specific structural information. In this work, we design an aspect-to-opinion graph and transform PAOTE into a graph parsing task. To exploit the interaction between term detection and pairing, we propose a novel mutually-aware interaction network (MAIN), which interactively updates the representations for term detection and pairing via graph sampling and convolution. Further, the word-word graph learned during training can be iteratively refined and gradually approaches the aspect-to-opinion graph. Experimental results on four benchmark datasets show that our proposed method significantly outperforms strong baselines with state-of-the-art performance and achieves a maximum increase of 2.01 points on the F1 metric. Further analysis demonstrates the advance of the aspect-to-opinion graph and the effectiveness of the mutually-aware interaction mechanism.