Cross-Lingual Transfer Robustness to Lower-Resource Languages on
  Adversarial Datasets
Shadi Manafi Nikhil Krishnaswamy
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.