Lingual-Agnostic Meta-Learning for Low-Resource Part-of-Speech Tagging
Pages 35 - 39
Editorial Notes
The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on May 30, 2021. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.
Abstract
Current deep learning based cross-lingual Part-of-Speech (POS) tagging methods are limited by their ability to achieve fast learning and generalization when the data in the target language is scarce. In this paper, we integrate a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better POS tagging. Based on the Model-Agnostic Meta-Learning framework (MAML), we propose a Lingual-Agnostic Meta-Learning (LAML) for cross-lingual low-resource POS tagging. The proposed LAML models cross-lingual POS tagging as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource languages. Also, different from the original MALM, LAML distinguishes parameters into shared parameters for represent learning and parameters to be adapted to different languages. We demonstrate that the proposed model outperforms the multilingual, joint-learning based approaches and enables us to train a competitive POS tagging system with only a fraction of samples.
Supplementary Material
Version of Record for "Lingual-Agnostic Meta-Learning for Low-Resource Part-of-Speech Tagging" by Zhang et al., 2020 The 8th International Conference on Information Technology: IoT and Smart City (ICIT '20).
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Published In
December 2020
266 pages
ISBN:9781450388559
DOI:10.1145/3446999
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Published: 09 April 2021
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