Albert Xlarge Vitaminc Mnli

🚀 ビタミンC事実検証モデル

このプロジェクトは、事実検証に使用するモデルを提供しています。対照的な証拠に基づいて堅牢な事実検証を実現し、複数のデータセットを処理することができます。

🚀 クイックスタート

このモデルは、対照的な証拠を用いて堅牢な事実検証を行うことができ、複数のデータセットを処理することが可能です。

📚 ドキュメント

本プロジェクトでは、論文 Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence（Schuster ら、NAACL 21）で提案されたモデルを使用しています。

詳細情報については、こちらを参照してください：https://github.com/TalSchuster/VitaminC

このモデルを使用する際には、この論文を引用してください。

データセット

• glue
• multi_nli
• tals/vitaminc

📄 ライセンス

BibTeX 引用および引用情報

@inproceedings{schuster-etal-2021-get,
    title = "Get Your Vitamin {C}! Robust Fact Verification with Contrastive Evidence",
    author = "Schuster, Tal  and
      Fisch, Adam  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.52",
    doi = "10.18653/v1/2021.naacl-main.52",
    pages = "624--643",
    abstract = "Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness{---}improving accuracy by 10{\%} on adversarial fact verification and 6{\%} on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.",
}