Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.
@article{arxiv.2110.07310,
title = {Solving Aspect Category Sentiment Analysis as a Text Generation Task},
author = {Jian Liu and Zhiyang Teng and Leyang Cui and Hanmeng Liu and Yue Zhang},
journal= {arXiv preprint arXiv:2110.07310},
year = {2021}
}