XeroAlign: Zero-Shot Cross-lingual Transformer Alignment

• URL: http://arxiv.org/abs/2105.02472v1
• Date: Thu, 6 May 2021 07:10:00 GMT
• Title: XeroAlign: Zero-Shot Cross-lingual Transformer Alignment
• Authors: Milan Gritta, Ignacio Iacobacci
• Abstract summary: We introduce a method for task-specific alignment of cross-lingual pretrained transformers such as XLM-R. XeroAlign uses translated task data to encourage the model to generate similar sentence embeddings for different languages. XLM-RA's text classification accuracy exceeds that of XLM-R trained with labelled data and performs on par with state-of-the-art models on a cross-lingual adversarial paraphrasing task.
• Score: 9.340611077939828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The introduction of pretrained cross-lingual language models brought decisive improvements to multilingual NLP tasks. However, the lack of labelled task data necessitates a variety of methods aiming to close the gap to high-resource languages. Zero-shot methods in particular, often use translated task data as a training signal to bridge the performance gap between the source and target language(s). We introduce XeroAlign, a simple method for task-specific alignment of cross-lingual pretrained transformers such as XLM-R. XeroAlign uses translated task data to encourage the model to generate similar sentence embeddings for different languages. The XeroAligned XLM-R, called XLM-RA, shows strong improvements over the baseline models to achieve state-of-the-art zero-shot results on three multilingual natural language understanding tasks. XLM-RA's text classification accuracy exceeds that of XLM-R trained with labelled data and performs on par with state-of-the-art models on a cross-lingual adversarial paraphrasing task.

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