DFKI-SLT/scifact

--- pretty_name: SciFact license: cc-by-nc-2.0 task_categories: - text-classification language: - en --- ## Dataset Description This is yet another attempt to have a useful dataset script for SciFact. In constrast to the [original script by AllenAI](https://huggingface.co/datasets/allenai/scifact), we provide two views (config names), each **consisting of all the data**: 1. `as_claims`: Instances are claims with its `cited_doc_ids` resolved to their actual document content, saved in `cited_docs`, and 2. `as_documents`: Instances are documents with its related claims, i.e. claims that originally linked the document at hand via `cited_doc_ids`. ### Citation Information ``` @inproceedings{wadden-etal-2020-fact, title = "Fact or Fiction: Verifying Scientific Claims", author = "Wadden, David and Lin, Shanchuan and Lo, Kyle and Wang, Lucy Lu and van Zuylen, Madeleine and Cohan, Arman and Hajishirzi, Hannaneh", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.609", doi = "10.18653/v1/2020.emnlp-main.609", pages = "7534--7550", } ```pretty_name: SciFact license: 知识共享署名-非商业性使用2.0通用许可（CC BY-NC 2.0) task_categories: - 文本分类 language: - 英语 ## 数据集描述 本脚本旨在为SciFact数据集提供一套实用的数据集加载方案。相较于AllenAI官方发布的[原始脚本](https://huggingface.co/datasets/allenai/scifact)，本脚本提供两种数据视角（配置名称），二者均覆盖完整数据集： 1. `as_claims`（主张视角）：数据实例为科学主张，其中`cited_doc_ids`（被引文档ID）已被解析为对应的实际文档内容，并存储于`cited_docs`字段中； 2. `as_documents`（文档视角）：数据实例为学术文档，附带其关联的科学主张——即最初通过`cited_doc_ids`与当前文档建立关联的所有科学主张。 ### 引用信息 @inproceedings{wadden-etal-2020-fact, title = "事实还是虚构：验证科学主张", author = "Wadden, David and Lin, Shanchuan and Lo, Kyle and Wang, Lucy Lu and van Zuylen, Madeleine and Cohan, Arman and Hajishirzi, Hannaneh", booktitle = "2020年自然语言处理经验方法会议（EMNLP）论文集", month = nov, year = "2020", address = "线上", publisher = "国际计算语言学协会", url = "https://aclanthology.org/2020.emnlp-main.609", doi = "10.18653/v1/2020.emnlp-main.609", pages = "7534--7550", }