Natural language understanding(NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. As a result, researchers have proposed to use pre-trained language model and multi-task learning to learn robust representations. However, aggressive fine-tuning often causes over-fitting and multi-task learning may favor tasks with significantly larger amounts data, etc. To address these problems, in this paper, we investigate model-agnostic meta-learning algorithm(MAML) in low-resource financial NLU tasks. Our contribution includes: 1. we explore the performance of MAML method with multiple types of tasks: GLUE datasets, SNLI, Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method with multiple single-type tasks: a real scenario stock price prediction problem with twitter text data. Our models achieve the state-of-the-art performance according to the experimental results, which demonstrate that our method can adapt fast and well to low-resource situations.

翻译：金融领域的自然语言理解（NLU）因缺乏标注数据及专业术语而具有挑战性。为此，研究者提出利用预训练语言模型和多任务学习来学习鲁棒表示。然而，激进微调常导致过拟合，多任务学习可能偏向数据量显著较大的任务等。针对这些问题，本文研究了低资源金融NLU任务中的模型无关元学习算法（MAML）。我们的贡献包括：1）探索MAML方法在多类型任务（GLUE数据集、SNLI、Sci-Tail、Financial PhraseBank）上的性能；2）研究MAML方法在多单类型任务（基于推特文本数据的真实场景股价预测问题）上的表现。实验结果表明，我们的模型达到了最先进的性能，证明了该方法能快速且良好地适应低资源场景。