Bi-syntax guided transformer network for aspect sentiment triplet extraction

Abstract

Aspect Sentiment Triplet Extraction is an emerging and challenging task that attempts to present a complete picture of aspect-based sentiment analysis. Prior research efforts mostly leverage various tagging schemes to extract the three elements in a triplet. However, these methods fail to explicitly model the complicated relations between aspects and opinions and the boundaries of multi-word aspects and opinions. In this paper, we propose a bi-syntax guided transformer network in an end-to-end manner to address these challenges. Firstly, we devise three types of representations, including sequence distance representation, constituency distance representation, and dependency distance representation, to learn the comprehensive language representation. Specifically, sequence distance representation utilizes sequence distance between words to enhance the contextual representation. Constituency distance representation adopts constituency distance between words in a constituency tree to capture the intra-span relation between words. Dependency distance representation employs dependency distance between words in a dependency tree to capture the long-distance relation between aspects and opinions. Extensive experiments are conducted on four benchmark datasets to validate the effectiveness of our method. The results demonstrate that the proposed approach achieves better performance than baseline methods. We conduct further detailed analysis to demonstrate that our method effectively handles multi-word terms and overlapping triplets.