  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.
