  We explore how weak supervision on abundant unlabeled data can be leveraged
to improve few-shot performance in aspect-based sentiment analysis (ABSA)
tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we
use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We
test the resulting model on three widely used ABSA datasets, before and after
fine-tuning. Our proposed method preserves the full fine-tuning performance
while showing significant improvements (15.84% absolute F1) in the few-shot
learning scenario for the harder tasks. In zero-shot (i.e., without
fine-tuning), our method outperforms the previous state of the art on the
aspect extraction sentiment classification (AESC) task and is, additionally,
capable of performing the harder aspect sentiment triplet extraction (ASTE)
task.
