Task and Sentiment Adaptation for Appraisal Tagging

Abstract

Sentiment analysis and opinion mining of the opinion-bearing text are important tasks in NLP. The Appraisal framework in systemic functional linguistics is a theory for analysing the linguistic patterns for expressing emotion and opinion. Manual annotation of appraisals however, requires linguistic expertise, and is costly and time-consuming. In this paper, we study how to automatically identify and tag appraisal text segments. We formulate the problem as a sequence tagging problem and propose a novel approach, Adaptive Appraisal (A2), which employs task and sentiment adapters on pre-trained language models for sequence appraisal tagging. Experiments on user comments, blogs and microblogs show that A2 outperforms baseline models and achieves good performance for cross-domain and cross-lingual settings. Source code for A2 is available at: https://github.com/ltian678/AA-code.git.