In this paper, we proposes a simple, tricky method to improve sentence representation of unsupervised contrastive learning. Even though contrastive learning has achieved great performances in both visual representation learning (VRL) and sentence representation learning (SRL) fields, we focus on the fact that there is a gap between characteristics and training dynamics of VRL and SRL. We first examine the role of temperature to bridge the gap between VRL and SRL, and find some temperature-dependent elements in SRL; i.e., a higher temperature causes overfitting of the uniformity while improving the alignment in earlier phase of training. Then, we design a temperature cool-down technique based on this observation, which helps PLMs to be more suitable for contrastive learning via preparation of uniform representation space. Our experimental results on widely-utilized benchmarks demonstrate the effectiveness and extensiblity of our method.

This paper aims to investigate the possibility of exploiting original semantic features of PLMs (pre-trained language models) during contrastive learning in the context of SRL (sentence representation learning). In the context of feature modification, we identified a method called IFM (implicit feature modification), which reduces the tendency of contrastive models for VRL (visual representation learning) to rely on feature-suppressing shortcut solutions. We observed that IFM did not work well for SRL, which may be due to differences between the nature of VRL and SRL. We propose BYOP, which boosts well-represented features, taking the opposite idea of IFM, under the assumption that SimCSE’s dropout-noise-based augmentation may be too simple to modify high-level semantic features, and that the features learned by PLMs are semantically meaningful and should be boosted, rather than removed. Extensive experiments lend credence to the logic of BYOP, which considers the nature of SRL.

Contrastive learning has been successfully adopted in VRL (visual representation learning) by constructing effective contrastive pairs. A promising baseline SimCSE has made notable breakthroughs in unsupervised SRL (sentence representation learning) following the success of contrastive learning. However, considering the difference between VRL and SRL, there is still room for designing a novel contrastive framework specially targeted for SRL. We pro- pose a novel angle-based similarity function for contrastive objective. By examining the gra- dient of our contrastive objective, we show that an angle-based similarity function incites better training dynamics on SRL than the off-the-shelf cosine similarity: (1) effectively pulling a posi- tive instance toward an anchor instance in the early stage of training and (2) not excessively repelling a false negative instance during the middle of training. Our experimental results on widely-utilized benchmarks demonstrate the ef- fectiveness and extensibility of our novel angle- based approach. Subsequent analyses establish its improved sentence representation power.