# Multiple Interactive Attention Networks for Aspect-Based Sentiment Classification
## 4 Experiment
### Model Comparisons
ASGCN(Aspect-specific Graph Convolutional Networks): This model proposes to exploit syntactical dependency structures within a sentence and resolves the long-range multi-word dependency issue for Aspect-Based Sentiment Classification. ASGCN posits that GCN is suitable for Aspect-Based Sentiment Classification and proposes a novel aspect-specific GCN model [34]. We compared MIN with other models on different datasets; the results are shown in Table 3.  
As shown in Table 3, the ATAE-LSTM model performed the worst among all the above models; one of the important reasons was that it relied on the LSTM to obtain the hidden state. Although ATAE-LSTM used the attention mechanism, it still could not comprehensively context analytical modeling. Our model also used a lighter GRU than LSTM, and MIN did not depend entirely on GRU. IAN performed better than ATAE-LSTM. IAN interactively learned attention in the context words and target words. But its target word and context word interactions were still coarse-grained, which might cause loss of interactive information. So, our model used a more fine-grained way to obtain \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**RestIt4**} & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Twitter**} & \multicolumn{2}{c}{**RestIt5**} & \multicolumn{2}{c}{**RestIt6**} \\ \hline  & **Acc 1** & **F1** 2 & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline
**ATAE-LSTM** & 0.7720 & - & 0.6870 & - & - & - & - & - & \\ \hline
**IAN** & 0.7860 & - & 0.7210 & - & 0.7250 & 0.7081 & 0.7854 & 0.5265 & 0.8474 & 0.5521 \\ \hline
**PBAN** & 0.8116 & - & 0.7412 & - & - & - & - & - & - & \\ \hline
**TNet-LF** & 0.8079 & 0.7084 & 0.7601 & 0.7147 & 0.7468 & 0.7336 & 0.7847 & 0.5947 & 0.8907 & 0.7043 \\ \hline
**TNet-ATT** & 0.8339 & 0.7566 & 0.7536 & 0.7202 & 0.7861 & 0.7772 & - & - & - \\ \hline
**BERT-PT** & 0.8495 & 0.7696 & 0.7807 & 0.7508 & - & - & - & - & - & \\ \hline
**CDT** & 0.8230 & 0.7402 & 0.7719 & 0.7299 & 0.7466 & 0.7366 & & & \\ \hline
**ASGCN** & 0.8086 & 0.7219 & 0.7262 & 0.6672 & 0.7105 & 0.6945 & 0.7834 & 0.6078 & 0.8833 & 0.6748 \\ \hline
**MIN** & 0.8268 & 0.7405 & 0.7978 & 0.7549 & 0.7384 & 0.7237 & 0.8284 & 0.6926 & 0.8912 & 0.6867 \\ \hline \hline \end{tabular} \({}^{1}\) Accuracy, 2 Macro-F1.  
\end{table} 
Table 3: The experimental results. interactive information. PBAN had better results than the model above because they noticed that the context word in different positions would have different effects on the target word, and our model took this into account. ASGCN proposed a novel aspect-specific GCN model, and it was the first to use the GCN for emotion classification. CDT performed better than ASGCN; CDT exploited a Bi-LSTM to learn representations for features of a sentence and further enhanced the embeddings with a GCN, which operated directly on the dependency tree of the sentence. It propagated both contextual and dependency information from opinion words to aspect words, offering discriminative properties for supervision. TNet-ATT proposed a progressive self-supervised attention mechanism algorithm based on TNet-LF. In these models, TNet-ATT performed best on the Twitter dataset, probably because many sentences in the TWITTER dataset were not standardized, and TNet-ATT could design different supervision signals for different situations. BERT-PT performed best in the restaurant reviews dataset, probably because they proposed BERT post-training based on pre-training BERT, and BERT post-training could better adjust BERT from both the domain and the task. Besides, the model connected questions and comments as input, predicted the probability of each word in the comment at the beginning and end of the answer, and then calculated the loss with the position of the real answer.