  The combination of multilingual pre-trained representations and cross-lingual
transfer learning is one of the most effective methods for building functional
NLP systems for low-resource languages. However, for extremely low-resource
languages without large-scale monolingual corpora for pre-training or
sufficient annotated data for fine-tuning, transfer learning remains an
under-studied and challenging task. Moreover, recent work shows that
multilingual representations are surprisingly disjoint across languages,
bringing additional challenges for transfer onto extremely low-resource
languages. In this paper, we propose MetaXL, a meta-learning based framework
that learns to transform representations judiciously from auxiliary languages
to a target one and brings their representation spaces closer for effective
transfer. Extensive experiments on real-world low-resource languages - without
access to large-scale monolingual corpora or large amounts of labeled data -
for tasks like cross-lingual sentiment analysis and named entity recognition
show the effectiveness of our approach. Code for MetaXL is publicly available
at github.com/microsoft/MetaXL.

Computation and Language;Machine Learning