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The explosion in novel NLP word embedding and deep learning techniques hasinduced significant endeavors into potential applications. One of thesedirections is in the financial sector. Although there is a lot of work done instate-of-the-art models like GPT and BERT, there are relatively few works onhow well these methods perform through fine-tuning after being pre-trained, aswell as info on how sensitive their parameters are. We investigate theperformance and sensitivity of transferred neural architectures frompre-trained GPT-2 and BERT models. We test the fine-tuning performance based onfreezing transformer layers, batch size, and learning rate. We find theparameters of BERT are hypersensitive to stochasticity in fine-tuning and thatGPT-2 is more stable in such practice. It is also clear that the earlier layersof GPT-2 and BERT contain essential word pattern information that should bemaintained.

Sensitivity Analysis on Transferred Neural Architectures of BERT and GPT-2 for Financial Sentiment Analysis