Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks
This work addresses performance issues in low-resource NLU for researchers and practitioners, but it is incremental as it applies existing meta-learning methods to a known bottleneck.
The paper tackled the problem of sub-optimal performance in low-resource natural language understanding tasks by exploring meta-learning algorithms like MAML, showing that the proposed models outperform strong baselines on the GLUE benchmark and adapt efficiently to new tasks.
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.