{
    "paper_id": "2020",
    "header": {
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        "date_generated": "2023-01-19T09:42:19.021599Z"
    },
    "title": "",
    "authors": [],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "",
    "pdf_parse": {
        "paper_id": "2020",
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        "abstract": [],
        "body_text": [
            {
                "text": "With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.)we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "To ensure accuracy, this content must be verified. However, the volume of information precludes human moderators from doing so. It is paramount to research automated means to verify accuracy and consistency of information published online and the downstream systems (such as Question Answering,Search and Digital Personal Assistants) which rely on it. The FEVER series of workshops has been a venue for ongoing research in this area. ",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Introduction",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "Please note that the FEVER 2020 Workshop will be held virtually. The exact times and links to each presentation can be found at https://fever.ai ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conference Program",
                "sec_num": null
            }
        ],
        "bib_entries": {},
        "ref_entries": {
            "FIGREF0": {
                "text": "University of Copenhagen) Jon Roozenbeek (University of Cambridge) Noam Slonim (IBM) Philip Resnik (University of Maryland) Dilek Hakkani-Tur (Amazon) Program Committee: Isabelle Augenstein (University of Copenhagen) Tuhin Chakrabarty (Columbia University) Diego Esteves (University of Bonn) Ivan Habernal (UKP Lab, Technische Universit\u00e4t Darmstadt) Andreas Hanselowski (UKP lab, Technische Universit\u00e4t Darmstadt) Alexandre Klementiev (Amazon) Nayeon Lee (Hong Kong University of Science and Technology) Pranava Swaroop Madhyastha (University of Sheffield) Christopher Malon (NEC Laboratories America) Marie-Francine Moens (KU Leuven) Yixin Nie (UNC) Farhad Nooralahzadeh (University of Oslo) Wolfgang Otto (GESIS -Leibniz-Institute for the Social Sciences in Cologne) Ankur Padia (University of Maryland, Baltimore County) Tamara Polajnar (University of Cambridge) Laura Rimell (DeepMind) Jodi Schneider (UIUC) Diarmuid \u00d3 S\u00e9aghdha (Apple) Kevin Small (Amazon) Motoki Taniguchi (Fuji Xerox) Paolo Torroni (Alma Mater -Universit\u00e0 di Bologna) Zeerak Waseem (University of Sheffield) v",
                "type_str": "figure",
                "num": null,
                "uris": null
            },
            "TABREF0": {
                "html": null,
                "text": "Simple Compounded-Label Training for Fact Extraction and Verification Yixin Nie, Lisa Bauer and Mohit Bansal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Stance Prediction and Claim Verification: An Arabic Perspective Jude Khouja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction Yang Zhou, Tong Zhao and Meng Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Takehito Utsuro and Yasuhide Kawada . . . . . . . . . 26 Language Models as Fact Checkers? Nayeon Lee, Belinda Li, Sinong Wang, Wen-tau Yih, Hao Ma and Madian Khabsa . . . . . . . . . . . . 36 Maintaining Quality in FEVER Annotation Leon Derczynski, Julie Binau and Henri Schulte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Distilling the Evidence to Augment Fact Verification Models Beatrice Portelli, Jason Zhao, Tal Schuster, Giuseppe Serra and Enrico Santus . . . . . . . . . . . . . . . . 47 Julie Binau and Henri Schulte Distilling the Evidence to Augment Fact Verification Models Beatrice Portelli, Jason Zhao, Tal Schuster, Giuseppe Serra and Enrico Santus Integration of (Un)structured World Knowledge In Task Oriented Conversations Dilek Hakkani-Tur",
                "content": "<table><tr><td>Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehen-sion by BERT Poster Session A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction Yang Zhou, Tong Zhao and Meng Jiang Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT Tengyang Chen, Hongyu Li, Miho Kasamatsu, Takehito Utsuro and Yasuhide Tengyang Chen, Hongyu Li, Miho Kasamatsu, 9th July (continued) Kawada</td></tr><tr><td>Language Models as Fact Checkers?</td></tr><tr><td>Nayeon Lee, Belinda Li, Sinong Wang, Wen-tau Yih, Hao Ma and Madian Khabsa</td></tr><tr><td>Maintaining Quality in FEVER Annotation</td></tr><tr><td>Leon Derczynski, Closing Remarks</td></tr><tr><td>FEVER Organizers</td></tr><tr><td>x</td></tr></table>",
                "type_str": "table",
                "num": null
            }
        }
    }
}