A multi-round MRC framework incorporating prompt learning for aspect sentiment triple extraction

Aspect-based sentiment triplet extraction (ASTE) task is a burgeoning subtask of aspect-based sentiment analysis (ABSA), which involves extracting aspect terms, opinion expressions, and related sentiment polarities from texts. However, previous pipeline methods for solving the ASTE task are susceptible to error propagation, while end-to-end sequence labeling methods have not fully utilized the given context. Additionally, existing machine reading comprehension (MRC)-based methods struggle to understand the complex grammatical structure and correspondence between aspect terms and opinion expressions, and fail to fully exploit the deep relationship between aspect-opinion pairs and sentiment polarity. To address these challenges, we propose the PromptReader, a multi-round MRC framework incorporating prompt learning. Specifically, we first design two rounds of MRC-based queries, where each round of queries consists of a pair of static and dynamic queries incorporating part-of-speech features and syntactic dependency information, aiming to jointly learn from two opposite perspectives to identify aspect-opinion pairs. Then, a round of queries based on prompt learning is designed, which contains a pair of dynamic queries, aiming to jointly learn under two different degrees of constraints to better predict sentiment polarity. Comprehensive experiments on four widely recognized benchmark datasets demonstrate that PromptReader surpasses the state-of-the-art methods by a significant margin.