Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective

• URL: http://arxiv.org/abs/2509.01147v1
• Date: Mon, 01 Sep 2025 05:49:49 GMT
• Title: Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective
• Authors: Zhihao Zhang, Sophia Yat Mei Lee, Dong Zhang, Shoushan Li, Guodong Zhou,
• Abstract summary: Cross-lingual Named Entity Recognition aims to transfer knowledge from high-resource languages to low-resource languages.<n>Existing zero-shot CL-NER approaches primarily focus on Latin script language (LSL), where shared linguistic features facilitate effective knowledge transfer.<n>For non-Latin script language (NSL), such as Chinese and Japanese, performance often degrades due to deep structural differences.<n>We propose an entity-aligned translation (EAT) approach. Leveraging large language models (LLMs), EAT employs a dual-translation strategy to align entities between NSL and English.
• Score: 29.24475373988723
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cross-lingual Named Entity Recognition (CL-NER) aims to transfer knowledge from high-resource languages to low-resource languages. However, existing zero-shot CL-NER (ZCL-NER) approaches primarily focus on Latin script language (LSL), where shared linguistic features facilitate effective knowledge transfer. In contrast, for non-Latin script language (NSL), such as Chinese and Japanese, performance often degrades due to deep structural differences. To address these challenges, we propose an entity-aligned translation (EAT) approach. Leveraging large language models (LLMs), EAT employs a dual-translation strategy to align entities between NSL and English. In addition, we fine-tune LLMs using multilingual Wikipedia data to enhance the entity alignment from source to target languages.

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