Representation and Labeling Gap Bridging for Cross-lingual Named Entity Recognition

Abstract

Cross-lingual Named Entity Recognition (NER) aims to address the challenge of data scarcity in low-resource languages by leveraging knowledge from high-resource languages. Most current work relies on general multilingual language models to represent text, and then uses classic combined tagging (e.g., B-ORG) to annotate entities; However, this approach neglects the lack of cross-lingual alignment of entity representations in language models, and also ignores the fact that entity spans and types have varying levels of labeling difficulty in terms of transferability. To address these challenges, we propose a novel framework, referred to as DLBri, which addresses the issues of representation and labeling simultaneously. Specifically, the proposed framework utilizes progressive contrastive learning with source-to-target oriented sentence pairs to pre-finetune the language model, resulting in improved cross-lingual entity-aware representations. Additionally, a decomposition-then-combination procedure is proposed, which separately transfers entity span and type, and then combines their information, to reduce the difficulty of cross-lingual entity labeling. Extensive experiments on 13 diverse language pairs confirm the effectiveness of DLBri.