@inproceedings{peng-etal-2021-domain,
    title = "Is Domain Adaptation Worth Your Investment? Comparing {BERT} and {F}in{BERT} on Financial Tasks",
    author = "Peng, Bo  and
      Chersoni, Emmanuele  and
      Hsu, Yu-Yin  and
      Huang, Chu-Ren",
    editor = "Hahn, Udo  and
      Hoste, Veronique  and
      Stent, Amanda",
    booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.econlp-1.5/",
    doi = "10.18653/v1/2021.econlp-1.5",
    pages = "37--44",
    abstract = "With the recent rise in popularity of Transformer models in Natural Language Processing, research efforts have been dedicated to the development of domain-adapted versions of BERT-like architectures. In this study, we focus on FinBERT, a Transformer model trained on text from the financial domain. By comparing its performances with the original BERT on a wide variety of financial text processing tasks, we found continual pretraining from the original model to be the more beneficial option. Domain-specific pretraining from scratch, conversely, seems to be less effective."
}