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.
@article{arxiv.2202.01924,
title = {Zero-Shot Aspect-Based Sentiment Analysis},
author = {Lei Shu and Hu Xu and Bing Liu and Jiahua Chen},
journal= {arXiv preprint arXiv:2202.01924},
year = {2022}
}