The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in limited-resource setting and demonstrate its effectiveness in two different settings: few-shot, cross-lingual adaptation to previously unseen languages; and multilingual joint training when limited target-language data is available during training. We conduct a systematic comparison of several meta-learning methods, investigate multiple settings in terms of data availability and show that meta-learning thrives in settings with a heterogeneous task distribution. We propose a simple, yet effective adjustment to existing meta-learning methods which allows for better and more stable learning, and set a new state of the art on several languages while performing on-par on others, using only a small amount of labeled data.
@article{arxiv.2101.11302,
title = {Multilingual and cross-lingual document classification: A meta-learning approach},
author = {Niels van der Heijden and Helen Yannakoudakis and Pushkar Mishra and Ekaterina Shutova},
journal= {arXiv preprint arXiv:2101.11302},
year = {2021}
}