Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
TABLE V: Ablation study of the MAN model
['[BOLD] Model', '[BOLD] Restaurant  [BOLD] ACC', '[BOLD] Restaurant  [BOLD] Macro-F1', '[BOLD] Hotel  [BOLD] ACC', '[BOLD] Hotel  [BOLD] Macro-F1']
In this section, we show the experiment results of different variants of our model and examine the effectiveness of the key components (e.g. Position-aware attention mechanism and regularization terms) on the model performance via an extensive ablation study. Comparing the results of MAN variants, the accuracy and macro-F1 measures of the MAN w/o Pos-Attention drop substantially on both data sets. It demonstrates the importance of contextual words position information, encoding the relevance between contextual words in review texts, on predicting the review sentiment polarities. In addition, the overall performance of MAN is also superior to all the variant models without the orthogonal regularization terms, which demonstrates the effectiveness to diversify the two different kinds of attention weights from different aspect attention encoders.