  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.
