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The main goal behind state-of-the-art pre-trained multilingual models such asmultilingual BERT and XLM-R is enabling and bootstrapping NLP applications inlow-resource languages through zero-shot or few-shot cross-lingual transfer.However, due to limited model capacity, their transfer performance is theweakest exactly on such low-resource languages and languages unseen duringpre-training. We propose MAD-X, an adapter-based framework that enables highportability and parameter-efficient transfer to arbitrary tasks and languagesby learning modular language and task representations. In addition, weintroduce a novel invertible adapter architecture and a strong baseline methodfor adapting a pre-trained multilingual model to a new language. MAD-Xoutperforms the state of the art in cross-lingual transfer across arepresentative set of typologically diverse languages on named entityrecognition and causal commonsense reasoning, and achieves competitive resultson question answering. Our code and adapters are available at AdapterHub.ml

MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer