@inproceedings{zhou-etal-2020-probabilistic,
    title = "A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction",
    author = "Zhou, Yang  and
      Zhao, Tong  and
      Jiang, Meng",
    editor = "Christodoulopoulos, Christos  and
      Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Mittal, Arpit",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.fever-1.3/",
    doi = "10.18653/v1/2020.fever-1.3",
    pages = "18--25",
    abstract = "Textual patterns (e.g., Country`s president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data."
}