  Self-training (ST) has prospered again in language understanding by
augmenting the fine-tuning of pre-trained language models when labeled data is
insufficient. However, it remains challenging to incorporate ST into
attribute-controllable language generation. Augmented by only self-generated
pseudo text, generation models over-emphasize exploitation of the previously
learned space, suffering from a constrained generalization boundary. We revisit
ST and propose a novel method, DuNST to alleviate this problem. DuNST jointly
models text generation and classification with a shared Variational AutoEncoder
and corrupts the generated pseudo text by two kinds of flexible noise to
disturb the space. In this way, our model could construct and utilize both
pseudo text from given labels and pseudo labels from available unlabeled text,
which are gradually refined during the ST process. We theoretically demonstrate
that DuNST can be regarded as enhancing exploration towards the potential real
text space, providing a guarantee of improved performance. Experiments on three
controllable generation tasks show that DuNST could significantly boost control
accuracy while maintaining comparable generation fluency and diversity against
several strong baselines.
