Using Selective Masking as a Bridge between Pre-training and Fine-tuning

TL;DR

This paper introduces a task-specific masking strategy during BERT pre-training, which improves downstream task performance by emphasizing important words through selective masking.

Contribution

It proposes a novel selective masking method that tailors pre-training to specific tasks, enhancing the transferability of language models.

Findings

Selective masking outperforms random masking in downstream tasks

Task-specific word importance improves model fine-tuning

Method enhances BERT's adaptability to various NLP tasks

Abstract

Pre-training a language model and then fine-tuning it for downstream tasks has demonstrated state-of-the-art results for various NLP tasks. Pre-training is usually independent of the downstream task, and previous works have shown that this pre-training alone might not be sufficient to capture the task-specific nuances. We propose a way to tailor a pre-trained BERT model for the downstream task via task-specific masking before the standard supervised fine-tuning. For this, a word list is first collected specific to the task. For example, if the task is sentiment classification, we collect a small sample of words representing both positive and negative sentiments. Next, a word's importance for the task, called the word's task score, is measured using the word list. Each word is then assigned a probability of masking based on its task score. We experiment with different masking functions that assign the probability of masking based on the word's task score. The BERT model is further trained on MLM objective, where masking is done using the above strategy. Following this standard supervised fine-tuning is done for different downstream tasks. Results on these tasks show that the selective masking strategy outperforms random masking, indicating its effectiveness.