@inproceedings{pratapa-etal-2020-constrained,
    title = "{C}onstrained {F}act {V}erification for {FEVER}",
    author = "Pratapa, Adithya  and
      Jayanthi, Sai Muralidhar  and
      Nerella, Kavya",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.emnlp-main.629/",
    doi = "10.18653/v1/2020.emnlp-main.629",
    pages = "7826--7832",
    abstract = "Fact-verification systems are well explored in the NLP literature with growing attention owing to shared tasks like FEVER. Though the task requires reasoning on extracted evidence to verify a claim{'}s factuality, there is little work on understanding the reasoning process. In this work, we propose a new methodology for fact-verification, specifically FEVER, that enforces a closed-world reliance on extracted evidence. We present an extensive evaluation of state-of-the-art verification models under these constraints."
}