@inproceedings{zhuang-ren-2022-yet,
    title = "Yet at the {F}in{NLP}-2022 {ERAI} Task: Modified models for evaluating the Rationales of Amateur Investors",
    author = "Zhuang, Yan  and
      Ren, Fuji",
    editor = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.finnlp-1.17/",
    doi = "10.18653/v1/2022.finnlp-1.17",
    pages = "132--135",
    abstract = "The financial reports usually reveal the recent development of the company and often cause the volatility in the company`s share price. The opinions causing higher maximal potential profit and lower maximal loss can help the amateur investors choose rational strategies. FinNLP-2022 ERAI task aims to quantify the opinions' potentials of leading higher maximal potential profit and lower maximal loss. In this paper, different strategies were applied to solve the ERAI tasks. Valinna {\textquoteleft}RoBERTa-wwm' showed excellent performance and helped us rank second in {\textquoteleft}MPP' label prediction task. After integrating some tricks, the modified {\textquoteleft}RoBERTa-wwm' outperformed all other models in {\textquoteleft}ML' ranking task."
}