The Impact of Language Adapters in Cross-Lingual Transfer for NLU

Modular deep learning has been proposed for the efficient adaption of pre-trained models to new tasks, domains and languages. In particular, combining language adapters with task adapters has shown potential where no supervised data exists for a language. In this paper, we explore the role of language adapters in zero-shot cross-lingual transfer for natural language understanding (NLU) benchmarks. We study the effect of including a target-language adapter in detailed ablation studies with two multilingual models and three multilingual datasets. Our …

cross-lingual cs.cl data deep learning domains explore impact language languages modular nlu paper pre-trained models role tasks transfer zero-shot