Teaching Others is Teaching Yourself Regularization For Controllable Language Models

Large-scale pre-trained language models have achieved great success on natural language generation tasks. However, it is difficult to control the pre-trained language models to generate sentences with the expected attribute such as topic and sentiment. Recent efforts on controllable language generation employ an additional attribute classifier, which guides the generation of large-scale pre-trained language models, have been shown to be efficient in controllable language generation. These methods are named ''classifier-guided language models'' (CGLMs). However, we find that the probabilities predicted by the attribute classifiers usually approaches 0 or 1, which make it hard to distinguish sentences with different matching degrees to the expected attribute. The problem is named \textit{the biased probability distribution} (BPD) problem. To address the problem, we investigate different methods for adjusting probability distribution and propose a ''Teaching Others is Teaching Yourself'' (TOTY) regularization method to smooth the probability distribution. Experiments on sentiment control and topic control tasks show that CGLMs can get better performance with guiding classifiers trained with TOTY.