Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
Table 3: An ablation study shows the effect of explicit target information.
['Dataset layer', 'Laptop 3', 'Laptop 4', 'Laptop 5', 'Restaurant 3', 'Restaurant 4', 'Restaurant 5']
On average, the accuracy of TD-GAT-GloVe increased by 1.2 percentage points. Capturing aspect-related information explicitly across layer is also useful for the BERT-based model as well. Even though the target information has been embedded in the BERT representation because of the contextual language modeling, TD-GAT-BERT still outperforms the GAT-BERT model. On average, the explicit target information contributes 0.95 percentage points to the final performance of the TD-GAT-BERT.