对抗数据集上跨语言迁移对低资源语言的鲁棒性
Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets
AI总结：
本文在13对高资源与低资源语言上评估MBERT和XLM-R在对抗扰动下的跨语言迁移表现，发现NER迁移能力主要取决于实体块重叠程度，且跨语言迁移对部分扰动更具鲁棒性。
摘要：
多语言语言模型（MLLMs）展现出强大的跨语言迁移能力，即能够利用在源语言中获取的信息并将其应用于目标语言。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。本研究旨在考察源语言应用于目标语言时的有效性，特别是在对输入测试集进行扰动的情况下。我们在13对语言上进行了评估，每对语言包含一种高资源语言（HRL）和一种低资源语言（LRL），二者之间具有地理、谱系或借用关系。我们在两个任务中、在一系列不同的扰动下，评估了两个知名的多语言语言模型——MBERT和XLM-R——在这些语言对上的表现，涵盖低资源语言原生设置和跨语言迁移设置。我们的研究结果表明，NER跨语言迁移在很大程度上取决于实体块的重叠程度。如果源语言和目标语言拥有更多共同的实体，则迁移能力更强。使用跨语言迁移的模型对输入的某些扰动也表现出一定程度的更强鲁棒性，这可能表明其具备利用源自高资源语言的更强表征的能力。我们的研究为跨语言迁移及其对NLP应用的影响提供了有价值的见解，并强调了在不同语言之间使用多语言语言模型时需要考虑语言细微差别和潜在局限性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
