论文标题
基于零方面的情感分析
Zero-Shot Aspect-Based Sentiment Analysis
论文作者
论文摘要
基于方面的情感分析（ABSA）通常需要注释的数据以进行监督培训/微调。将ABSA扩展到大量新域是一个巨大的挑战。本文旨在训练一个统一模型，该模型可以执行零射击ABSA，而无需为新域使用任何带注释的数据。我们提出了一种称为“对比后培训”的方法，以审查自然语言推断（玉米）。后来的ABSA任务可以施放到NLI中以进行零射传递。我们在ABSA任务上评估玉米，从方面提取（AE），方面情感分类（ASC）到端到端的基于方面的情感分析（E2E ABSA），这表明可以在没有任何人类注释的ABSA数据的情况下进行ABSA。
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.
