Large-scale cross-lingual pre-trained language models (xPLMs) have shown
effectiveness in cross-lingual sequence labeling tasks (xSL), such as
cross-lingual machine reading comprehension (xMRC) by transferring knowledge
from a high-resource language to low-resource languages. Despite the great
success, we draw an empirical observation that there is a training objective
gap between pre-training and fine-tuning stages: e.g., mask language modeling
objective requires local understanding of the masked token and the
span-extraction objective requires global understanding and reasoning of the
input passage/paragraph and question, leading to the discrepancy between
pre-training and xMRC. In this paper, we first design a pre-training task
tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM)
to eliminate the objective gap in a self-supervised manner. Second, we present
ContrAstive-Consistency Regularization (CACR), which utilizes contrastive
learning to encourage the consistency between representations of input parallel
sequences via unsupervised cross-lingual instance-wise training signals during
pre-training. By these means, our methods not only bridge the gap between
pretrain-finetune, but also enhance PLMs to better capture the alignment
between different languages. Extensive experiments prove that our method
achieves clearly superior results on multiple xSL benchmarks with limited
pre-training data. Our methods also surpass the previous state-of-the-art
methods by a large margin in few-shot data settings, where only a few hundred
training examples are available.

本篇论文提出 Cross-lingual Language Informative Span Masking (CLISM) 和 ContrAstive-Consistency Regularization (CACR) 两种方法来缩小预先训练和微调阶段之间的差距，并以多语言对齐为目标对跨语言机器阅读理解（xMRC）进行改进。实验结果表明，这些方法在多个 xSL 基准测试中取得了显著优越的结果，并且在只有几百个训练示例可用的少量数据设置中超过了以前的最新方法。