  Self-training provides an effective means of using an extremely small amount
of labeled data to create pseudo-labels for unlabeled data. Many
state-of-the-art self-training approaches hinge on different regularization
methods to prevent overfitting and improve generalization. Yet they still rely
heavily on predictions initially trained with the limited labeled data as
pseudo-labels and are likely to put overconfident label belief on erroneous
classes depending on the first prediction. To tackle this issue in text
classification, we introduce LST, a simple self-training method that uses a
lexicon to guide the pseudo-labeling mechanism in a linguistically-enriched
manner. We consistently refine the lexicon by predicting confidence of the
unseen data to teach pseudo-labels better in the training iterations. We
demonstrate that this simple yet well-crafted lexical knowledge achieves
1.0-2.0% better performance on 30 labeled samples per class for five benchmark
datasets than the current state-of-the-art approaches.
