Towards Simple and Efficient Task-Adaptive Pre-training for Text Classification

TL;DR

This paper proposes a simple, efficient method for domain adaptation in BERT models by selectively pre-training only the embedding layer during task-adaptive pre-training, reducing computational costs while maintaining performance.

Contribution

The study introduces a novel approach of training only the embedding layer during TAPT, significantly reducing parameters and computational effort without sacrificing accuracy.

Findings

Training only the embedding layer achieves comparable performance to full model training.

The method reduces training parameters by 78%.

It effectively adapts models to target domain vocabulary.

Abstract

Language models are pre-trained using large corpora of generic data like book corpus, common crawl and Wikipedia, which is essential for the model to understand the linguistic characteristics of the language. New studies suggest using Domain Adaptive Pre-training (DAPT) and Task-Adaptive Pre-training (TAPT) as an intermediate step before the final finetuning task. This step helps cover the target domain vocabulary and improves the model performance on the downstream task. In this work, we study the impact of training only the embedding layer on the model's performance during TAPT and task-specific finetuning. Based on our study, we propose a simple approach to make the intermediate step of TAPT for BERT-based models more efficient by performing selective pre-training of BERT layers. We show that training only the BERT embedding layer during TAPT is sufficient to adapt to the vocabulary of the target domain and achieve comparable performance. Our approach is computationally efficient, with 78\% fewer parameters trained during TAPT. The proposed embedding layer finetuning approach can also be an efficient domain adaptation technique.