ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models

TL;DR

This paper investigates the impact of [MASK] tokens in Masked Language Models, identifies the corrupted semantics problem, and introduces ExLM, an enhanced-context MLM that improves semantic understanding and task performance.

Contribution

The paper proposes ExLM, a novel MLM that expands [MASK] tokens to increase context capacity and mitigate corrupted semantics, advancing the effectiveness of masked language modeling.

Findings

ExLM outperforms baseline models in text and SMILES modeling tasks.

Expanded context modeling enriches semantic representations.

ExLM reduces semantic multimodality in MLMs.

Abstract

Masked Language Models (MLMs) have achieved remarkable success in many self-supervised representation learning tasks. MLMs are trained by randomly masking portions of the input sequences with [MASK] tokens and learning to reconstruct the original content based on the remaining context. This paper explores the impact of [MASK] tokens on MLMs. Analytical studies show that masking tokens can introduce the corrupted semantics problem, wherein the corrupted context may convey multiple, ambiguous meanings. This problem is also a key factor affecting the performance of MLMs on downstream tasks. Based on these findings, we propose a novel enhanced-context MLM, ExLM. Our approach expands [MASK] tokens in the input context and models the dependencies between these expanded states. This enhancement increases context capacity and enables the model to capture richer semantic information, effectively mitigating the corrupted semantics problem during pre-training. Experimental results demonstrate that ExLM achieves significant performance improvements in both text modeling and SMILES modeling tasks. Further analysis confirms that ExLM enriches semantic representations through context enhancement, and effectively reduces the semantic multimodality commonly observed in MLMs.