Disentangled Contrastive Learning for Learning Robust Textual Representations
This addresses robustness issues in NLP for applications requiring stable performance under input variations, though it is incremental as it builds on existing contrastive learning frameworks.
The paper tackles the vulnerability of transformer models to small input permutations by proposing a disentangled contrastive learning method that separately optimizes uniformity and alignment of textual representations without negative sampling, achieving better results on NLP benchmarks and improvements in invariance tests and adversarial attacks.
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs. Intuitively, the representations should be similar in the feature space with subtle input permutations, while large variations occur with different meanings. This motivates us to investigate the learning of robust textual representation in a contrastive manner. However, it is non-trivial to obtain opposing semantic instances for textual samples. In this study, we propose a disentangled contrastive learning method that separately optimizes the uniformity and alignment of representations without negative sampling. Specifically, we introduce the concept of momentum representation consistency to align features and leverage power normalization while conforming the uniformity. Our experimental results for the NLP benchmarks demonstrate that our approach can obtain better results compared with the baselines, as well as achieve promising improvements with invariance tests and adversarial attacks. The code is available in https://github.com/zxlzr/DCL.