Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
Table 4: The model size (number of parameters) of our model as well as baselines.
['Models', 'Model size (×106)']
Using the same dimension of hidden states, our TD-GAT-GloVe has a lower model size compared to these LSTM-based methods. MemNet is the model ranks the first in terms of the model size. The size of TD-GAT-BERT increases by 0.3×106 because of the linear projection layer applied on the input word representations. When we switch from GloVe embeddings to BERT representations, the training time for a three-layer TD-GAT model on the restaurant dataset only increases from 1.12 seconds/epoch to 1.15 seconds/epoch. On the contrary, fine-tuning the BERT model takes about 226.50 seconds for each training epoch. Training our TD-GAT-BERT model requires much fewer computation resources and less time compared to fine-tuning the original BERT model.