Learning to Augment for Data-scarce Domain BERT Knowledge Distillation

Abstract

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of Natural Language Processing (NLP) tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment data for BERT Knowledge Distillation in target domains with scarce labeled data, by learning a cross-domain manipulation scheme that automatically augments the target domain with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced controller to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on different NLP tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ~13.3%) when only a few labeled examples available.

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“…Feng et al [ 7 ] proposed a method to learn to augment data-scarce BERT-domain knowledge distillation, learning a cross-domain manipulation scheme that automatically augments the target with the help of resource-rich source domains to tackle the problem of performance degradation due to data scarcity in the target domain. Aiming at the same problem, Ma et al [ 8 ] presented a novel two-step domain-adaptation framework based on curriculum learning and domain-discriminative data selection.…”