Label-guided Learning for Text Classification
Table 5: Comparison results of one label embedding and label embedding space (represented by five embeddings by default in this study). LguidedLearn-1: LguidedLearn with using only one embedding to represent each label.
['Model', '20NG', 'R8', 'R52', 'Ohsumed', 'MR']
Therefore, it’s not reasonable to use only one embedding to represent a label. In the study, we extend label embedding to label embedding space which is represented by a series of embeddings (embedding matrix). From the results we can see that the performance first improves as increasing the number of embeddings per label from one to five, and then decreases with using more than ten embeddings per label. Because at first increasing the label embedding size will increase labels‘ representation ability; but using too many embeddings also will decrease the label discriminative ability. The best choice of the size of label embedding matrix is dependent on datasets. For convenience and avoiding excessively fine tuning parameters, in all experiments we simply use five embeddings to represent each label (see the section of Experimental settings). The comparing results illustrate that using embedding matrix (used by LguideLearn) can obtain stable and fruitful improvements, compared to using one label embedding (usually used by previous label embedding model, such as LEAM).