UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
This work addresses the performance gap for under-resourced languages, particularly those with unseen scripts, in multilingual language models, which is a problem for NLP practitioners working with diverse linguistic data.
The authors propose data-efficient methods to adapt multilingual language models to low-resource languages and unseen scripts. Their approach leverages matrix factorization and shared vocabulary items, leading to substantial performance gains for languages with unseen scripts and improvements for low-resource languages in covered scripts.
Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due to limited capacity and large differences in pretraining data sizes, there is a profound performance gap between resource-rich and resource-poor target languages. The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining. In this work, we propose a series of novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts. Relying on matrix factorization, our methods capitalize on the existing latent knowledge about multiple languages already available in the pretrained model's embedding matrix. Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called lexically overlapping tokens) shared between mBERT's and target language vocabulary. Our adaptation techniques offer substantial performance gains for languages with unseen scripts. We also demonstrate that they can yield improvements for low-resource languages written in scripts covered by the pretrained model.