Meta-Learning for Natural Language Understanding under Continual Learning Framework

• URL: http://arxiv.org/abs/2011.01452v1
• Date: Tue, 3 Nov 2020 03:41:10 GMT
• Title: Meta-Learning for Natural Language Understanding under Continual Learning Framework
• Authors: Jiacheng Wang, Yong Fan, Duo Jiang, Shiqing Li
• Abstract summary: We implement the model-agnostic meta-learning (MAML) and Online aware Meta-learning (OML) meta-objective under the continual framework for NLU tasks. We validate our methods on selected SuperGLUE and GLUE benchmark.
• Score: 2.620911206953405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural network has been recognized with its accomplishments on tackling various natural language understanding (NLU) tasks. Methods have been developed to train a robust model to handle multiple tasks to gain a general representation of text. In this paper, we implement the model-agnostic meta-learning (MAML) and Online aware Meta-learning (OML) meta-objective under the continual framework for NLU tasks. We validate our methods on selected SuperGLUE and GLUE benchmark.

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