Prompt template construction by Average Gradient Search with External Knowledge for aspect sentimental analysis

Abstract

Aspect-based Sentiment Analysis (ABSA) aims to predict the sentiment polarity towards a particular aspect in a sentence. Most of the existing methods construct neural networks or fine-tune the pre-trained language models. However, it is difficult to fully utilize the knowledge of language model learned during pretraining process, and it usually performs worse in few-shot experiment. Prompt learning can alleviate the above problems effectively. Manual prompt template construction methods are adopted usually but it leads to high costs and low efficiency, and there are seldom prompt templates applicable to ABSA task. We propose a method for ABSA prompt template construction, named Average Gradient Search with External Knowledge Based on KNN. The vocabulary list of the pre-trained language model is built as a KD-Tree, and the KNN algorithm is used to search the best prompt template on KD-Tree. For the sentiment polarity prediction, the best prompt template is used to wrap the ABSA data, converting the classification task to generative task, which enables the language model to fully leverage the learned knowledge from the pre-training stage. Moreover, the constructed verbalizer incorporates external knowledge to provide label words of each class with extensive semantic coverage and lower subjective bias. The comparison experimental results on SemEval2014 Restaurant and Laptop datasets demonstrate that the proposed method outperforms the existing SOTA models. Further, the few-shot experimental results indicate that, with only a small number of training samples, the proposed method achieves comparable or even better performance than the baseline model trained on the full dataset.