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

Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, Sebastian Ruder


Abstract
The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pre-training. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pre-trained multilingual model to a new language. MAD-X outperforms the state of the art in cross lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering. Our code and adapters are available at AdapterHub.ml.
Anthology ID:
2020.emnlp-main.617
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7654–7673
Language:
URL:
https://aclanthology.org/2020.emnlp-main.617
DOI:
10.18653/v1/2020.emnlp-main.617
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.617.pdf
Video:
 https://slideslive.com/38938991