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

Abstract

Language models are pre-trained using large corpora of generic data like book corpus, com- mon 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 in- termediate step of TAPT for BERT-based mod- els 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.