Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the ability to leverage information acquired in a source language and apply it to a target language. These capabilities find practical applications in well-established Natural Language Processing (NLP) tasks such as Named Entity Recognition (NER). This study aims to investigate the effectiveness of a source language when applied to a target language, particularly in the context of perturbing the input test set. We evaluate on 13 pairs of languages, each including one high-resource language (HRL) and one low-resource language (LRL) with a geographic, genetic, or borrowing relationship. We evaluate two well-known MLLMs--MBERT and XLM-R--on these pairs, in native LRL and cross-lingual transfer settings, in two tasks, under a set of different perturbations. Our findings indicate that NER cross-lingual transfer depends largely on the overlap of entity chunks. If a source and target language have more entities in common, the transfer ability is stronger. Models using cross-lingual transfer also appear to be somewhat more robust to certain perturbations of the input, perhaps indicating an ability to leverage stronger representations derived from the HRL. Our research provides valuable insights into cross-lingual transfer and its implications for NLP applications, and underscores the need to consider linguistic nuances and potential limitations when employing MLLMs across distinct languages.

翻译：多语言语言模型（MLLMs）展现出稳健的跨语言迁移能力，即能将源语言中获取的信息应用于目标语言的能力。这些能力在命名实体识别（NER）等成熟的自然语言处理（NLP）任务中具有实际应用价值。本研究旨在探究源语言应用于目标语言时的有效性，特别是针对输入测试集施加扰动的场景。我们评估了13对语言组合，每对包含一个高资源语言（HRL）和一个低资源语言（LRL），且语言之间存在地理、谱系或借词关系。我们采用两种知名的MLLMs——MBERT和XLM-R——在原生LRL设置和跨语言迁移设置下，针对两类任务施加一组不同扰动进行评测。研究结果表明：NER跨语言迁移效果很大程度上依赖于实体块的交叠程度。若源语言与目标语言共有实体越多，迁移能力越强。采用跨语言迁移的模型似乎对特定输入扰动具有更强的鲁棒性，这可能表明其能够利用从HRL中学到的更强表征。本研究为跨语言迁移及其对NLP应用的影响提供了重要见解，并强调了在跨语言使用MLLMs时必须关注语言细微差异与潜在局限性。