@inproceedings{samarinas-etal-2021-improving,
    title = "Improving Evidence Retrieval for Automated Explainable Fact-Checking",
    author = "Samarinas, Chris  and
      Hsu, Wynne  and
      Lee, Mong Li",
    editor = "Sil, Avi  and
      Lin, Xi Victoria",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.naacl-demos.10/",
    doi = "10.18653/v1/2021.naacl-demos.10",
    pages = "84--91",
    abstract = "Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. We describe a three-stage automated fact-checking system, named Quin+, using evidence retrieval and selection methods. We demonstrate that using dense passage representations leads to much higher evidence recall in a noisy setting. We also propose two sentence selection approaches, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. Quin+ is able to verify open-domain claims using results from web search engines."
}