  Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated
data for supervised training/fine-tuning. It is a big challenge to scale ABSA
to a large number of new domains. This paper aims to train a unified model that
can perform zero-shot ABSA without using any annotated data for a new domain.
We propose a method called contrastive post-training on review Natural Language
Inference (CORN). Later ABSA tasks can be cast into NLI for zero-shot transfer.
We evaluate CORN on ABSA tasks, ranging from aspect extraction (AE), aspect
sentiment classification (ASC), to end-to-end aspect-based sentiment analysis
(E2E ABSA), which show ABSA can be conducted without any human annotated ABSA
data.
