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arXiv · 2505.11746

Token Masking Improves Transformer-Based Text Classification

Xianglong Xu·John Bowen·Rojin Taheri
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Abstract

While transformer-based models achieve strong performance on text classification, we explore whether masking input tokens can further enhance their effectiveness. We propose token masking regularization, a simple yet theoretically motivated method that randomly replaces input tokens with a special [MASK] token at probability p. This introduces stochastic perturbations during training, leading to implicit gradient averaging that encourages the model to capture deeper inter-token dependencies. Experiments on language identification and sentiment analysis -- across diverse models (mBERT, Qwen2.5-0.5B, TinyLlama-1.1B) -- show consistent improvements over standard regularization techniques. We identify task-specific optimal masking rates, with p = 0.1 as a strong general default. We attribute the gains to two key effects: (1) input perturbation reduces overfitting, and (2) gradient-level smoothing acts as implicit ensembling.

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Xianglong Xu, John Bowen, Rojin Taheri. 2025-05-16. Token Masking Improves Transformer-Based Text Classification. https://arxiv.org/abs/2505.11746

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