A fundamental task of fine-grained sentiment analysis is aspect term extraction.Supervised-learning approaches have demonstrated state-of-the art results for thistask; however, they underperform in few-shot scenarios, where labeled trainingdata is scarce. Prompt-based training has proven effective in few-shot sequenceclassification; however, it would not apply to token classification tasks. In thiswork we propose PATE (Prompt-based Aspect Term Extraction), a few-shotprompt-based method for the token classification task of aspect term extraction.We demonstrate that this method significantly outperforms the standard supervisedtraining approach in few-shot setups and make our code publicly available.