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The great majority of languages in the world are considered under-resourcedfor the successful application of deep learning methods. In this work, wepropose a meta-learning approach to document classification in limited-resourcesetting and demonstrate its effectiveness in two different settings: few-shot,cross-lingual adaptation to previously unseen languages; and multilingual jointtraining when limited target-language data is available during training. Weconduct a systematic comparison of several meta-learning methods, investigatemultiple settings in terms of data availability and show that meta-learningthrives in settings with a heterogeneous task distribution. We propose asimple, yet effective adjustment to existing meta-learning methods which allowsfor better and more stable learning, and set a new state of the art on severallanguages while performing on-par on others, using only a small amount oflabeled data.

Multilingual and cross-lingual document classification: A meta-learning approach