CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts

The architecture of CORT

摘要

Comparative opinion is a common linguistic phenomenon. The opinion is expressed by comparing multiple targets on a shared aspect, e.g., “camera A is better than camera B in picture quality”. Among the various subtasks in opinion mining, comparative opinion classification is relatively less studied. Current solutions use rules or classifiers to identify opinions, i.e., better, worse, or same, through feature engineering. Because the features are directly derived from the input sentence, these solutions are sensitive to the order of the targets mentioned in the sentence. For example, “camera A is better than camera B” means the same as “camera B is worse than camera A”; but the features of these two sentences are completely different. In this paper, we approach comparative opinion classification through prompt learning, taking the advantage of embedded knowledge in pre-trained language model. We design a twin framework with dual prompts, named CORT. This extremely simple model delivers state-of-the-art and robust performance on all benchmark datasets for comparative opinion classification. We believe CORT well serves as a new baseline for comparative opinion classification.

出版物
In Findings of the Association for Computational Linguistics:EMNLP 2022
Yequan Wang
Yequan Wang
人工智能研究员

我的研究兴趣包含预训练语言模型,对话系统和情感分析等。