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Building natural language processing systems for non standardized and lowresource languages is a difficult challenge. The recent success of large-scalemultilingual pretrained language models provides new modeling tools to tacklethis. In this work, we study the ability of multilingual language models toprocess an unseen dialect. We take user generated North-African Arabic as ourcase study, a resource-poor dialectal variety of Arabic with frequentcode-mixing with French and written in Arabizi, a non-standardizedtransliteration of Arabic to Latin script. Focusing on two tasks,part-of-speech tagging and dependency parsing, we show in zero-shot andunsupervised adaptation scenarios that multilingual language models are able totransfer to such an unseen dialect, specifically in two extreme cases: (i)across scripts, using Modern Standard Arabic as a source language, and (ii)from a distantly related language, unseen during pretraining, namely Maltese.Our results constitute the first successful transfer experiments on thisdialect, paving thus the way for the development of an NLP ecosystem forresource-scarce, non-standardized and highly variable vernacular languages.

Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi