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        <title>Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks</title>
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        <namePart type="given">Zi-Yi</namePart>
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    <abstract>Learning general representations of text is a fundamental problem for many natural language understanding (NLU) tasks. Previously, researchers have proposed to use language model pre-training and multi-task learning to learn robust representations. However, these methods can achieve sub-optimal performance in low-resource scenarios. Inspired by the recent success of optimization-based meta-learning algorithms, in this paper, we explore the model-agnostic meta-learning algorithm (MAML) and its variants for low-resource NLU tasks. We validate our methods on the GLUE benchmark and show that our proposed models can outperform several strong baselines. We further empirically demonstrate that the learned representations can be adapted to new tasks efficiently and effectively.</abstract>
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%0 Conference Proceedings
%T Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks
%A Dou, Zi-Yi
%A Yu, Keyi
%A Anastasopoulos, Antonios
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F dou-etal-2019-investigating
%X Learning general representations of text is a fundamental problem for many natural language understanding (NLU) tasks. Previously, researchers have proposed to use language model pre-training and multi-task learning to learn robust representations. However, these methods can achieve sub-optimal performance in low-resource scenarios. Inspired by the recent success of optimization-based meta-learning algorithms, in this paper, we explore the model-agnostic meta-learning algorithm (MAML) and its variants for low-resource NLU tasks. We validate our methods on the GLUE benchmark and show that our proposed models can outperform several strong baselines. We further empirically demonstrate that the learned representations can be adapted to new tasks efficiently and effectively.
%R 10.18653/v1/D19-1112
%U https://aclanthology.org/D19-1112
%U https://doi.org/10.18653/v1/D19-1112
%P 1192-1197

Markdown (Informal)

[Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks](https://aclanthology.org/D19-1112) (Dou et al., EMNLP-IJCNLP 2019)

• Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks (Dou et al., EMNLP-IJCNLP 2019)

ACL

• Zi-Yi Dou, Keyi Yu, and Antonios Anastasopoulos. 2019. Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1192–1197, Hong Kong, China. Association for Computational Linguistics.