BERT Based Cross-Task Sentiment Analysis with Adversarial Learning

Sentiment Analysis (SA) is an essential task in natural language processing. Generally, previous sentiment analysis models focus on a single subtask. However, a generalized SA agent is expected with the ability to learn knowledge from one task and use it in other relevant tasks. Consequently, we formulate this challenge as an unsupervised task adaption problem and propose TAL-IS, a simple and efficient approach to finetune cross-task SA model. In this approach, we use Task Adversarial Learning (TAL) with a BERT-specific Input Standardization (IS) scheme to obtain both emotion-discriminative and task-invariant contextual features. To the best of our knowledge, our work is the first attempt to propose a cross-task model for SA subtasks with unsupervised task adaption. Experiments show that our proposed model outperforms the general finetuning method and can learn knowledge effectively cross SA subtasks.