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

• URL: http://arxiv.org/abs/2101.11302v1
• Date: Wed, 27 Jan 2021 10:22:56 GMT
• Title: Multilingual and cross-lingual document classification: A meta-learning approach
• Authors: Niels van der Heijden, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova
• Abstract summary: We propose a meta-learning approach to document classification in limited-resource setting. We show effectiveness in two settings: few-shot, cross-lingual adaptation to previously unseen languages and multilingual joint training.
• Score: 24.66829920826166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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