Auto-verbalizer filtering for prompt-based aspect category detection

Abstract

Aspect category detection (ACD) is a basic task in sentiment analysis that aims to identify the specific aspect categories discussed in reviews. In the case of limited label resources, prompt-based models have shown promise in few-shot ACD. However, their current limitations lie in the manual selection or reliance on external knowledge for obtaining the verbalizer, a critical component of prompt learning that maps predicted words to final categories. To solve these issues, we propose an ACD method to automatically build the verbalizer in prompt learning. Our approach leverages the semantic expansion of category labels as prompts to automatically acquire initial verbalizer tokens. Additionally, we introduce an indicator mechanism for auto-verbalizer filtering to obtain reasonable verbalizer words and improve the predicting aspect category reliability of the method. In zero-shot task, our model exhibits an average performance improvement of 7.5% over the second-best model across four ACD datasets. For the other three few-shot tasks, the average performance improvement over the second-best model is approximately 2%. Notably, our method demonstrates effectiveness, particularly in handling general or miscellaneous category aspects.