LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding

TL;DR

LRC-BERT introduces a contrastive knowledge distillation approach with a gradient perturbation training architecture to create a compact, robust BERT model suitable for edge deployment, outperforming existing methods on GLUE benchmarks.

Contribution

The paper presents a novel contrastive distillation method and a gradient perturbation training architecture, enhancing model robustness and efficiency for natural language understanding.

Findings

LRC-BERT outperforms state-of-the-art distillation methods on GLUE datasets.

The contrastive distillation effectively captures intermediate layer distributions.

Gradient perturbation improves model robustness against adversarial attacks.

Abstract

The pre-training models such as BERT have achieved great results in various natural language processing problems. However, a large number of parameters need significant amounts of memory and the consumption of inference time, which makes it difficult to deploy them on edge devices. In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods. Furthermore, we introduce a gradient perturbation-based training architecture in the training phase to increase the robustness of LRC-BERT, which is the first attempt in knowledge distillation. Additionally, in order to better capture the distribution characteristics of the intermediate layer, we design a two-stage training method for the total distillation loss. Finally, by verifying 8 datasets on the General Language Understanding Evaluation (GLUE) benchmark, the performance of the proposed LRC-BERT exceeds the existing state-of-the-art methods, which proves the effectiveness of our method.