  For many business applications, we often seek to analyze sentiments
associated with any arbitrary aspects of commercial products, despite having a
very limited amount of labels or even without any labels at all. However,
existing aspect target sentiment classification (ATSC) models are not trainable
if annotated datasets are not available. Even with labeled data, they fall
short of reaching satisfactory performance. To address this, we propose simple
approaches that better solve ATSC with natural language prompts, enabling the
task under zero-shot cases and enhancing supervised settings, especially for
few-shot cases. Under the few-shot setting for SemEval 2014 Task 4 laptop
domain, our method of reformulating ATSC as an NLI task outperforms supervised
SOTA approaches by up to 24.13 accuracy points and 33.14 macro F1 points.
Moreover, we demonstrate that our prompts could handle implicitly stated
aspects as well: our models reach about 77% accuracy on detecting sentiments
for aspect categories (e.g., food), which do not necessarily appear within the
text, even though we trained the models only with explicitly mentioned aspect
terms (e.g., fajitas) from just 16 reviews - while the accuracy of the
no-prompt baseline is only around 65%.
