X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering

• URL: http://arxiv.org/abs/2104.09696v1
• Date: Tue, 20 Apr 2021 00:13:35 GMT
• Title: X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering
• Authors: Meryem M'hamdi, Doo Soon Kim, Franck Dernoncourt, Trung Bui, Xiang Ren, and Jonathan May
• Abstract summary: We propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for Natural Language Understanding (NLU) Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages.
• Score: 55.57776147848929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization ability is still inconsistent for typologically diverse languages and across different benchmarks. Recently, meta-learning has garnered attention as a promising technique for enhancing transfer learning under low-resource scenarios: particularly for cross-lingual transfer in Natural Language Understanding (NLU). In this work, we propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for NLU. Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages. Our analysis reveals that X-METRA-ADA can leverage limited data for faster adaptation.

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