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Zero-Shot Aspect-Based Sentiment Analysis

2022-02-04 00:51:46
Lei Shu, Jiahua Chen, Bing Liu, Hu Xu

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2202.01924

PDF

https://arxiv.org/pdf/2202.01924


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