Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance

• URL: http://arxiv.org/abs/2303.02841v2
• Date: Sun, 26 Mar 2023 19:45:25 GMT
• Title: Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance
• Authors: Bixing Yan, Shaoling Chen, Yuxuan He, Zhihan Li
• Abstract summary: We investigate model-agnostic meta-learning algorithm(MAML) in low-resource financial NLU tasks. Our models achieve the state-of-the-art performance according to the experimental results.
• Score: 1.863067234952186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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