Temporal Attention-Gated Model for Robust Sequence Classification
Table 2: Classification accuracy (%) on Stanford Sentiment TreeBank dataset when training with only the sentence-level labels. We conduct experiments on both binary and fine-grained (5-class) classification tasks. Note that our model outperforms all others in the task.
['[EMPTY]', 'Model', 'Binary', 'Fine-grained']
We conduct two sets of experiments to evaluate the performance of our model in comparison with the baseline models. Since our model is designed for unsegmented and possibly noisy sequences modeling, it is more suitable to only use sentence-level labels, although phrase-level labels are also provided in SST dataset. Our model achieves the best result in both binary classification task and fine-grained (5-class) task. LSTM and GRU outperform plain-RNN model due to the information-filtering capability performed by additional gates. It is worth mentioning that our model achieves better performance than LSTM with only half the hidden parameters.