Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e.g. in e-commerce, where data and insights from reviews can be leveraged to create value for businesses and customers. Recently, deep transfer-learning methods have been applied successfully to a myriad of Natural Language Processing (NLP) tasks, including ATSC. Building on top of the prominent the BERT language model, we approach ATSC by using a two-step procedure: Self-supervised domain-specific BERT language model finetuning, followed by supervised task-specific finetuning. Our findings on how to best exploit domain-specific language model finetuning enables us to produce new state-of-the-art performance on the SemEval 2014 Task 4 restaurants dataset. In addition, to explore the real-world robustness of our models, we perform cross-domain evaluation. We show that a cross-domain adapted BERT language model performs significantly better compared to strong baseline models like vanilla BERT-base and XLNet-base.

本文阐述了将领域自适应的 BERT 语言模型与半监督建模进行结合达到最优的 Aspect-Target Sentiment Classification ，并通过不同的数据集得出短语级别情感分类的最新成果，并通过交叉领域验证比较了自适应BERT的性能。

领域适应：基于BERT语言模型微调的方面-目标情感分类