Revisiting Aspect-Sentiment-Opinion Triplet Extraction: Detailed Analyses Towards a Simple and Effective Span-Based Model

Abstract

Aspect-based sentiment information extraction has attracted increasing attention in the research community of natural language processing. Various methods, such as sequence tagging, sequence-to-sequence generation and span-based extraction, have been proposed, which own different advantages and disadvantages. In this article, we revisit the span-based method for aspect-sentiment-opinion triplet extraction, by designing and analyzing a simple yet effective Span-based Model, called SMTFASTE. Our model leverages a tidily three-layer architecture, including a BERT-based encoding layer, a span representation layer and an aspect-sentiment-opinion prediction layer. In the experiments of two widely-used benchmarks (ASTE-V2 and ASOTE-V2), we find that our model outperforms a number of complicated state-of-the-art models in most evaluation metrics. Therefore, we conduct detailed analyses for our model, such as ablation studies of the core components of our model and the benefit of explicitly using context information, and obtain some insightful findings and conclusion. Through this study, we show that a simple span-based model is able to achieve competitive results without much feature and architecture engineering. Our model is easy to follow and we have opened our code to facilitate related research.