Albert Xlarge Vitaminc Mnli

🚀 維生素C事實驗證模型

該項目提供了用於事實驗證的模型，基於對比證據實現魯棒的事實驗證，可處理多種數據集。

📚 詳細文檔

本項目使用了論文 Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence（Schuster 等人，NAACL 21）中提出的模型。

更多詳細信息請參考：https://github.com/TalSchuster/VitaminC

使用此模型時，請引用該論文。

數據集

• glue
• multi_nli
• tals/vitaminc

📄 許可證

BibTeX 引用及引用信息

@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.",
}