Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective (2025)

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.

## introduction cross-lingual named entity recognition (cl-ner) aims to transfer knowledge from highresource source languages to low-resource target languages, so as to enhance the ner performance @xcite @xcite . recently, while zero-shot cl-ner (zcl-ner) approaches demonstrate strong performance on several low-resource languages @xcite @xcite , we observe an interesting phenomenon in prior approaches and different language scripts. previous zcl-ner approaches typically apply a teacher-student (t-s) learning framework, transferring english knowledge to the target language @xcite . this recasts translated english [en] german [de] chinese [zh] figure 1 : two examples: german, as lsl, tends to be translated more accurately into english due to their shared lexicon, making it more suitable for ner. in contrast, chinese, as nsl, faces inherent challenges in translation to english because of significant typological divergences. the translations are obtained by the source language into the same space as the target language to achieve zcl-ner. regarding the existing studies @xcite , they achieve competitive performance on the englishlike target languages that have shared vocabulary origins, as well as similar grammatical and syntactic structures such as german and french @xcite . for example, as illustrated in figure 1 , the german words "britische" and "fotografieren" are derived from the english words "british" and "photograph", respectively. we refer to such languages, which are closely related to english, as latin script language (lsl). however, as shown in our pilot experiments and previous reports @xcite , t-s approaches work not well on non-latin script language (nsl), such as chinese and japanese. this is mainly due to significant linguistic discrepancies between lsl and nsl, i.e., differences in scripts, grammar, and syntax @xcite , which are summarized in to this end, we believe that it is essential to mitigate the language gap between nsl and english. in this way, we can utilize the abundant english resources to improve zcl-ner performance on targeted nsl, which constitutes the core focus of this paper. as we know, translation appears to be the most intuitive approach to bridging the linguistic disparity @xcite . however, direct translation between english and nsl typically results in the key entity omissions due to various word misalignment issues @xcite . for instance, in figure 1 , the person entity "高明" in target language (chinese) is incorrectly translated as the adjective "clever" (by gpt-4o and deepseek in may 2025 with evidences in appendix). such translation inconsistencies hinder the progress of our task. to address the above issues, we propose an entity-aligned translation (eat) approach at dual levels with large language models (llms) for zcl-ner, with a focus on nsl as the target languages. different from t-s approaches, we recast the target nsl into the same space with english to achieve zcl-ner. specifically, we first leverage the powerful reasoning and interpretation abilities of llms to perform target-to-english forward translation using multi-round chain-of-thought (mr-cot). then, we extract potential entities from * https://en.m.wikipedia.org/wiki/iso_639-1 the translated english text using pre-trained english ner extractor. finally, to restore these entities in the target language, we design a backward translation process with mrcot, ensuring that this stage's translated entities correspond to the correct fragments in the original sentence. the process allows us to achieve zcl-ner without relying on parallel cross-lingual corpora. to further refine entity alignment, we fine-tune llms on english-oriented entity-aligned cross-lingual corpora (eacl), sourced from multilingual wikipedia. in general, our contributions are summarized: ## methodology in this section, we first introduce our proposed entity-aligned translation (eat) framework, as illustrated in figure 2 : dual translation, sourceoriented cross-lingual corpora collection, and entity-level alignment fine-tuning. then, we present two metrics to evaluate the correlation between translation quality and zcl-ner performance. ## conclusion we introduce a novel entity-aligned translation (eat) approach with llms for zero-shot crosslingual ner (zcl-ner) approach to mitigate the linguistic differences between non-latin script language (nsl) and english, so as to better leverage the rich english ner resources for multilingual ner tasks. in addition, we fine-tune the llm using collected source-oriented cross-lingual corpora to enhance entity alignments for better ner. furthermore, we employ bleu and information entropy to analyze the correlation between ner performance and translation ability.