{
 "id": "copenlu/sufficient_facts",
 "owner": "copenlu",
 "name": "sufficient_facts",
 "downloads": 81,
 "likes": 6,
 "last_modified": "2022-08-05T08:33:48.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": "mit",
 "licence_files": [
  "LICENSE"
 ],
 "sources": [
  {
   "id": "fever",
   "licence": "cc-by-sa-3.0",
   "found": true,
   "evidence": "https://huggingface.co/datasets/fever/fever/blob/2a74f2909caf2b8656343aeb8203e50bf84dcb56/README.md"
  },
  {
   "id": "hover",
   "licence": "cc-by-sa-4.0",
   "found": true,
   "evidence": "https://huggingface.co/datasets/hover-nlp/hover/blob/c0e43052759879b3461642ca6c0dd26658f47691/README.md"
  },
  {
   "id": "fever_gold_evidence",
   "licence": null,
   "found": false,
   "evidence": "https://huggingface.co/datasets/fever_gold_evidence"
  }
 ],
 "gated": false,
 "tasks": [
  "text-classification"
 ],
 "citation": "@article{10.1162/tacl_a_00486,\n    author = {Atanasova, Pepa and Simonsen, Jakob Grue and Lioma, Christina and Augenstein, Isabelle},\n    title = \"{Fact Checking with Insufficient Evidence}\",\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {10},\n    pages = {746-763},\n    year = {2022},\n    month = {07},\n    abstract = \"{Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, SufficientFacts1, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21\\\\% accuracy), whereas it is easiest for omitted date modifiers (63\\\\% accu",
 "signal": "conflict",
 "reason": "The declared licence (mit) may conflict with a source dataset's reported share-alike licence (cc-by-sa-3.0).",
 "revision": "58d5b361e47265cf85f2334800c66d9bb485029e",
 "stated_evidence": "https://huggingface.co/datasets/copenlu/sufficient_facts/blob/58d5b361e47265cf85f2334800c66d9bb485029e/README.md",
 "file_evidence": "https://huggingface.co/datasets/copenlu/sufficient_facts/blob/58d5b361e47265cf85f2334800c66d9bb485029e/LICENSE",
 "card_nc": false
}