@inproceedings{ghosh-naskar-2022-lipi-finnlp,
    title = "{LIPI} at the {F}in{NLP}-2022 {ERAI} Task: Ensembling Sentence Transformers for Assessing Maximum Possible Profit and Loss from Online Financial Posts",
    author = "Ghosh, Sohom  and
      Naskar, Sudip Kumar",
    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-1.13/",
    doi = "10.18653/v1/2022.finnlp-1.13",
    pages = "111--115",
    abstract = "Using insights from social media for making investment decisions has become mainstream. However, in the current era of information ex- plosion, it is essential to mine high-quality so- cial media posts. The FinNLP-2022 ERAI task deals with assessing Maximum Possible Profit (MPP) and Maximum Loss (ML) from social me- dia posts relating to finance. In this paper, we present our team LIPI`s approach. We ensem- bled a range of Sentence Transformers to quan- tify these posts. Unlike other teams with vary- ing performances across different metrics, our system performs consistently well. Our code is available here \url{https://github.com/sohomghosh/LIPI_ERAI_} FinNLP{\_}EMNLP- 2022/"
}