  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.
