Temporal Attention-Gated Model for Robust Sequence Classification
Table 3: Classification accuracy (%) on Stanford Sentiment TreeBank dataset when training with both phrase-level and sentence-level labels. Our TAGM achieves the best overall result.
['[EMPTY]', 'Model', 'Binary', 'Fine-grained', 'Overall Performance']
To have a fair comparison with the existing sentiment analysis models, we conduct the second set of experiments with both sentence-level and phrase-level labels. It shows that our model outperforms most of the existing models and achieves comparable accuracy with the state-of-the-art results. Our TAGM model actually obtains overall best results considering both binary and fine-grained cases. This is an encouraging result, in particular, since our model is not specifically designed towards NLP tasks.