BILEAT: a highly generalized and robust approach for unified aspect-based sentiment analysis: BILEAT.

Aspect-based sentiment analysis (ABSA) includes two subtasks, namely, aspect term extraction and aspect-level sentiment classification. Most existing works address these subtasks independently. Recently, many researchers have attempted to solve both the subtasks of ABSA with a unified framework. However, previous works have not focused on the generalization and robustness of such unified frameworks. This paper proposes a novel BERT-Based Interactive Learning with Ensemble Adversarial Training (BILEAT) to solve complete ABSA by using a unified tagging scheme. We build white-box adversarially post-trained domain knowledge BERT (WBDK-BERT) using a domain-specific dataset. During post-training, we regularize the training objective by adding perturbations in the embedding space to maximize the adversarial loss, enhancing the generalization and robustness of WBDK-BERT. BILEAT uses WBDK-BERT to generate contextualized embeddings and produce collaborative signals through interactive learning. Further, to build a highly reliable model, we generate adversarial examples using a black-box technique. These adversarial examples are grammatically fluent, semantically coherent with original input, and can mislead the neural network. Our proposed model is trained using original inputs and such adversarial examples in a combined way. Experimental results demonstrate that WBDK-BERT and black-box adversarial examples complement each other, and combining these two helps BILEAT become highly generalized and robust compared to existing methods. To the best of our knowledge, this is the first study that generates quality adversarial examples and evaluates the robustness of models for unified ABSA¹. [ABSTRACT FROM AUTHOR]