Jointly Trained Sequential Labeling and Classificationby Sparse Attention Neural Networks
Figure 4: Sentence-level accuracy of our latent-variable model on TREC, compared with various neural network-based models.
['Model', 'Sent.\xa0Acc.']
We further show the performance of our jointly trained model when the sequence label information is missing. Tab. There is a small increase of error rate when sequence labels are unobserved, but our model still outperforms existing models. Similarly, Fig. Our model outperforms others after adding sparse attention. In Fig. We list a few examples that the sparse attention is better than the one without sparse attention constraint. The labels on the right side are mis-classified by the model without sparse attention constraint. The label on the left side of the arrow is the ground truth.