DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
Table 2: Test accuracy of fine-grained sentiment analysis on Stanford Sentiment Treebank (SST) dataset.
['[BOLD] Model', '[BOLD] Test Accu']
To the best of our knowledge, DiSAN improves the last best accuracy (given by CNN-Tensor) by 0.52%. Compared to tree-based models with heavy use of the prior structure, e.g., MV-RNN, RNTN and Tree-LSTM, DiSAN outperforms them by 7.32%, 6.02% and 0.72%, respectively. Additionally, DiSAN achieves better performance than CNN-based models. More recent works tend to focus on lexicon-based sentiment analysis, by exploring sentiment lexicons, negation words and intensity words. Nonetheless, DiSAN still outperforms these fancy models, such as NCSL (+0.62%) and LR-Bi-LSTM (+1.12%).