Towards Robust and Efficient Contrastive Textual Representation Learning

There has been growing interest in representation learning for text data, based on theoretical arguments and empirical evidence. One important direction involves leveraging contrastive learning to improve learned representations. We propose an application of contrastive learning for intermediate textual feature pairs, to explicitly encourage the model to learn more distinguishable representations. To overcome the learner's degeneracy due to vanishing contrasting signals, we impose Wasserstein constraints on the critic via spectral regularization. Finally, to moderate such an objective from overly regularized training and to enhance learning efficiency, with theoretical justification, we further leverage an active negative-sample-selection procedure to only use high-quality contrast examples. We evaluate the proposed method over a wide range of natural language processing applications, from the perspectives of both supervised and unsupervised learning. Empirical results show consistent improvement over baselines.