Table 6: Experimental results of LCF-ATEPC on Laptop and Restaurant data sets using domain-adapted pretrained BERT.
['[BOLD] Model', '[BOLD] Laptop  [ITALIC] F1 [ITALIC] ate', '[BOLD] Laptop  [ITALIC] Accapc', '[BOLD] Laptop  [ITALIC] F1 [ITALIC] apc', '[BOLD] Restaurant  [ITALIC] F1 [ITALIC] ate', '[BOLD] Restaurant  [ITALIC] Accapc', '[BOLD] Restaurant  [ITALIC] F1 [ITALIC] apc']
The BERT-BASE model is trained on a large-scale general corpus, so the fine-tuning during process during training process is significant and inevitable for BERT-based models. Meanwhile, the ABSA datasets commonly benchmarked are generally small with the domain-specific characteristic, the effect of BERT-BASE model on the most ABSA datasets can be further improved through domain-adaption. Domain adaption is a effective technique while integrating the pre-trained BERT-BASE model. By further training the BERT-BASE model in a domain-related corpus similar to or homologous to the target ABSA dataset, then domain-related pretrained BERT model can be obtained. The accuracy benchmark in the classical Restaurant achieving more than 90%, which means that the LCF-ATEPC is the first ABSA-oriented model obtained up to 90% accuracy on the Restaurant dataset. In addition, experimental result on the Laptop dataset also prove the effectiveness of domain-adaption in multi-task learning. Besides, the experimental results on the laptop dataset also validate the effectiveness of domain-adapted BERT model for ABSA multi-task learning.