Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification
Table 2: Comparison of our model with various baselines.
['Model', 'Restaurant Acc', 'Restaurant Macro-F1', 'Laptop Acc', 'Laptop Macro-F1']
In order to alleviate problems caused by parsing errors, a straightforward solution is to combine the GCN model over dependency trees with a self-attention based sequence model. The self-attention sequence model enables the direct interaction between the aspect term and opinion words so that it can take care of the situation where the aspect term is far away from the opinion words on the dependency tree. Classification. From this table, we observe that: our model achieves the best results in Restaurant and Laptop domains, outperforms the current state-of-the-art by 1.3% and 0.3% in terms of accuracy, and 2.7% and 0.5% in terms of Macro-F1. Applying BERT-large model as the encoder in our model further boosts the performance on both datasets. Notice that the performance gains of two metrics from BERT-large model, i.e. accuracy and F1, are very different on two datasets. This is because accuracy is like precision, while F1 depends on both precision and recall.