Domain-Specific Intermediate Tasks and Zero-Shot Cross-Lingual Transfer in XNLI

Abstract

Abstract: Zero-shot cross-lingual transfer is a central task in multilingual NLP, allowing models trained in languages with more sufficient training resources to generalize to other low-resource languages. Earlier efforts on this task use parallel corpora, bilingual dictionaries, or other annotated alignment data to improve cross-lingual transferability, which are typically expensive to obtain. In this paper, we propose a simple yet effective method, SALT, to improve the zero-shot cross-lingual transfer of the multilingual pretrained language models without the help of such external data. By incorporati

Research Question

What is the impact of domain-specific intermediate tasks (e.g., legal or medical) on zero-shot cross-lingual transfer performance in XNLI compared to general language understanding tasks?

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