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The introduction of pretrained cross-lingual language models brought decisiveimprovements to multilingual NLP tasks. However, the lack of labelled task datanecessitates a variety of methods aiming to close the gap to high-resourcelanguages. Zero-shot methods in particular, often use translated task data as atraining signal to bridge the performance gap between the source and targetlanguage(s). We introduce XeroAlign, a simple method for task-specificalignment of cross-lingual pretrained transformers such as XLM-R. XeroAlignuses translated task data to encourage the model to generate similar sentenceembeddings for different languages. The XeroAligned XLM-R, called XLM-RA, showsstrong improvements over the baseline models to achieve state-of-the-artzero-shot results on three multilingual natural language understanding tasks.XLM-RA's text classification accuracy exceeds that of XLM-R trained withlabelled data and performs on par with state-of-the-art models on across-lingual adversarial paraphrasing task.

XeroAlign: Zero-Shot Cross-lingual Transformer Alignment