SimRP: Syntactic and Semantic Similarity Retrieval Prompting Enhances Aspect Sentiment Quad Prediction

Abstract

Aspect Sentiment Quad Prediction (ASQP) is the most complex subtask of Aspect-based Sentiment Analysis (ABSA), aiming to predict all sentiment quadruples within the given sentence. Due to the complexity of sentence syntaxes and the diversity of sentiment expressions, generative methods gradually become the mainstream approach in ASQP. However, existing generative models are constrained in the effectiveness of demonstrations. Semantically similar demonstrations help in judging sentiment categories and polarities but may confuse the model in recognizing aspect and opinion terms, which are more related to sentence syntaxes. To this end, we first develop Syn2Vec, a method for calculating syntactic vectors to support the retrieval of syntactically similar demonstrations. Then, we propose Syntactic and Semantic Similarity Retrieval Prompting (SimRP) to construct effective prompts by retrieving the most related demonstrations that are syntactically and semantically similar. With these related demonstrations, pre-trained generative models, especially Large Language Models (LLMs), can fully release their potential to recognize sentiment quadruples. Extensive experiments in Supervised Fine-Tuning (SFT) and In-context Learning (ICL) paradigms demonstrate the effectiveness of SimRP. Furthermore, we find that LLMs' capabilities in ASQP are severely underestimated by biased data annotations and the exact matching metric. We propose a novel constituent subtree-based fuzzy metric for more accurate and rational quadruple recognition.