Masked language modeling (MLM) is one of the key sub-tasks in vision-language pretraining. In the cross-modal setting, tokens in the sentence are masked at random, and the model predicts the masked tokens given the image and the text. In this paper, we observe several key disadvantages of MLM in this setting. First, as captions tend to be short, in a third of the sentences no token is sampled. Second, the majority of masked tokens are stop-words and punctuation, leading to under-utilization of the image. We investigate a range of alternative masking strategies specific to the cross-modal setting that address these shortcomings, aiming for better fusion of text and image in the learned representation. When pre-training the LXMERT model, our alternative masking strategies consistently improve over the original masking strategy on three downstream tasks, especially in low resource settings. Further, our pre-training approach substantially outperforms the baseline model on a prompt-based probing task designed to elicit image objects. These results and our analysis indicate that our method allows for better utilization of the training data.

本文研究在跨模态预训练中使用遮蔽语言建模（Masked Language Modeling，简称MLM）的一些问题，提出了一些针对这些问题的替代遮蔽策略，在LXMERT模型预训练时，我们的替代策略始终优于原始遮蔽策略，特别是在低资源设置下，我们的预训练方法显著优于基准模型，并且通过对影像对象的特定标记任务的评估，我们的结果和分析表明，该方法允许更好地利用训练数据。

视觉语言领域数据效率掩码语言建模