A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference
This addresses the challenge of learning discriminative representations with limited training data for natural language inference, which is incremental as it builds on contrastive learning methods.
The paper tackles the problem of low-resource natural language inference by proposing a multi-level supervised contrastive learning framework, achieving an average accuracy improvement of 3.1% over other models on public datasets.
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis). Recently, low-resource natural language inference has gained increasing attention, due to significant savings in manual annotation costs and a better fit with real-world scenarios. Existing works fail to characterize discriminative representations between different classes with limited training data, which may cause faults in label prediction. Here we propose a multi-level supervised contrastive learning framework named MultiSCL for low-resource natural language inference. MultiSCL leverages a sentence-level and pair-level contrastive learning objective to discriminate between different classes of sentence pairs by bringing those in one class together and pushing away those in different classes. MultiSCL adopts a data augmentation module that generates different views for input samples to better learn the latent representation. The pair-level representation is obtained from a cross attention module. We conduct extensive experiments on two public NLI datasets in low-resource settings, and the accuracy of MultiSCL exceeds other models by 3.1% on average. Moreover, our method outperforms the previous state-of-the-art method on cross-domain tasks of text classification.