Cross-lingual named entity recognition (CrossNER) faces challenges stemming
from uneven performance due to the scarcity of multilingual corpora, especially
for non-English data. While prior efforts mainly focus on data-driven transfer
methods, a significant aspect that has not been fully explored is aligning both
semantic and token-level representations across diverse languages. In this
paper, we propose Multi-view Contrastive Learning for Cross-lingual Named
Entity Recognition (mCL-NER). Specifically, we reframe the CrossNER task into a
problem of recognizing relationships between pairs of tokens. This approach
taps into the inherent contextual nuances of token-to-token connections within
entities, allowing us to align representations across different languages. A
multi-view contrastive learning framework is introduced to encompass semantic
contrasts between source, codeswitched, and target sentences, as well as
contrasts among token-to-token relations. By enforcing agreement within both
semantic and relational spaces, we minimize the gap between source sentences
and their counterparts of both codeswitched and target sentences. This
alignment extends to the relationships between diverse tokens, enhancing the
projection of entities across languages. We further augment CrossNER by
combining self-training with labeled source data and unlabeled target data. Our
experiments on the XTREME benchmark, spanning 40 languages, demonstrate the
superiority of mCL-NER over prior data-driven and model-based approaches. It
achieves a substantial increase of nearly +2.0 $F_1$ scores across a broad
spectrum and establishes itself as the new state-of-the-art performer.

通过多视角对比学习，在跨语言命名实体识别任务中，对于语义和标记级别的表示进行对齐，扩展了不同语言之间的实体投影，同时结合自学习和标注源数据与无标注目标数据的方法，在 40 种语言的 XTREME 基准测试上实现了近 + 2.0 F1 分数的显著提高，并成为新的现有技术的顶级表现者。

mCL-NER: 多视角对比学习的跨语言命名实体识别

mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view  Contrastive Learning

Fine-tuning pre-trained cross-lingual language models can transfer
task-specific supervision from one language to the others. In this work, we
propose to improve cross-lingual fine-tuning with consistency regularization.
Specifically, we use example consistency regularization to penalize the
prediction sensitivity to four types of data augmentations, i.e., subword
sampling, Gaussian noise, code-switch substitution, and machine translation. In
addition, we employ model consistency to regularize the models trained with two
augmented versions of the same training set. Experimental results on the XTREME
benchmark show that our method significantly improves cross-lingual fine-tuning
across various tasks, including text classification, question answering, and
sequence labeling.

本研究提出采用一致性正则化改进跨语言 fine-tuning 的方法，通过示例一致性正则化对子词采样、高斯噪声、代码切换和机器翻译四种数据增广方式的敏感性进行惩罚，并采用模型的一致性正则化方法对同一训练集上经过增广的两个版本的模型进行规范化。在 XTREME 基准测试中，实验结果表明，我们的方法显着提高了跨语言 fine-tuning 在各种任务中的性能，包括文本分类、问题回答和序列标注。