Span-level bidirectional retention scheme for aspect sentiment triplet extraction

Abstract

The objective of the Aspect Sentiment Triplet Extraction (ASTE) task is to identify triplets of (aspect, opinion, sentiment) from user-generated reviews. The current study does not extensively integrate the interaction between word pairs and aspect-opinion pairs during the learning process at the granularity of sentence analysis. Furthermore, the bidirectional inference for the triplet, along with the parallel computing approach for long-span texts, also fail to achieve efficient unification. We introduce a new perspective: Span-level Bidirectional Retention Scheme(SBRS) for Aspect Sentiment Triplet Extraction model. The model comprises two pathways. The first pathway involves extracting effective aspect-opinion pair outcomes via two progressive submodules that operate on words and word pairs at varying scales. Building on the first pathway, the second pathway senses the interaction information of word pairs through bidirectional recursion and combines an efficient parallel computing approach. This combination allows the model to utilize three features – context, semantics, and relationship – to accurately identify the sentimental orientation. Thus, the two pathways facilitate the learning of relation-aware representations of word pairs. We carried out experiments on two public datasets, showing an average enhancement of 3.34% and 1.72% in F1 scores compared to the most recent baselines models, and multiple experiments from diverse angles proved the model’s superiority.