共享任务

James Thorne, Andreas Vlachos, O. Cocarascu, Christos Christodoulopoulos, Arpit Mittal
{"title":"共享任务","authors":"James Thorne, Andreas Vlachos, O. Cocarascu, Christos Christodoulopoulos, Arpit Mittal","doi":"10.18653/v1/D19-6601","DOIUrl":null,"url":null,"abstract":"We present the results of the second Fact Extraction and VERification (FEVER2.0) Shared Task. The task challenged participants to both build systems to verify factoid claims using evidence retrieved from Wikipedia and to generate adversarial attacks against other participant’s systems. The shared task had three phases: building, breaking and fixing. There were 8 systems in the builder’s round, three of which were new qualifying submissions for this shared task, and 5 adversaries generated instances designed to induce classification errors and one builder submitted a fixed system which had higher FEVER score and resilience than their first submission. All but one newly submitted systems attained FEVER scores higher than the best performing system from the first shared task and under adversarial evaluation, all systems exhibited losses in FEVER score. There was a great variety in adversarial attack types as well as the techniques used to generate the attacks, In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.","PeriodicalId":153447,"journal":{"name":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"70","resultStr":"{\"title\":\"The FEVER2.0 Shared Task\",\"authors\":\"James Thorne, Andreas Vlachos, O. Cocarascu, Christos Christodoulopoulos, Arpit Mittal\",\"doi\":\"10.18653/v1/D19-6601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present the results of the second Fact Extraction and VERification (FEVER2.0) Shared Task. The task challenged participants to both build systems to verify factoid claims using evidence retrieved from Wikipedia and to generate adversarial attacks against other participant’s systems. The shared task had three phases: building, breaking and fixing. There were 8 systems in the builder’s round, three of which were new qualifying submissions for this shared task, and 5 adversaries generated instances designed to induce classification errors and one builder submitted a fixed system which had higher FEVER score and resilience than their first submission. All but one newly submitted systems attained FEVER scores higher than the best performing system from the first shared task and under adversarial evaluation, all systems exhibited losses in FEVER score. There was a great variety in adversarial attack types as well as the techniques used to generate the attacks, In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.\",\"PeriodicalId\":153447,\"journal\":{\"name\":\"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"70\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/D19-6601\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/D19-6601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 70

摘要

我们提出了第二个事实提取和验证(FEVER2.0)共享任务的结果。这项任务要求参与者既要构建系统,使用从维基百科检索到的证据来验证虚假声明,又要对其他参与者的系统产生对抗性攻击。共享任务分为三个阶段:构建、破坏和修复。在构建者的回合中有8个系统,其中3个是这个共享任务的新合格提交,5个对手生成了旨在诱导分类错误的实例,一个构建者提交了一个固定的系统,该系统比他们第一次提交的系统具有更高的FEVER分数和弹性。除了一个新提交的系统外,所有新提交的系统的FEVER得分都高于第一个共享任务中表现最好的系统,在对抗性评估下,所有系统的FEVER得分都有所下降。在本文中,我们展示了共享任务的结果和系统的总结,突出了参与系统之间的共性和创新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The FEVER2.0 Shared Task
We present the results of the second Fact Extraction and VERification (FEVER2.0) Shared Task. The task challenged participants to both build systems to verify factoid claims using evidence retrieved from Wikipedia and to generate adversarial attacks against other participant’s systems. The shared task had three phases: building, breaking and fixing. There were 8 systems in the builder’s round, three of which were new qualifying submissions for this shared task, and 5 adversaries generated instances designed to induce classification errors and one builder submitted a fixed system which had higher FEVER score and resilience than their first submission. All but one newly submitted systems attained FEVER scores higher than the best performing system from the first shared task and under adversarial evaluation, all systems exhibited losses in FEVER score. There was a great variety in adversarial attack types as well as the techniques used to generate the attacks, In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Veritas Annotator: Discovering the Origin of a Rumour Neural Multi-Task Learning for Stance Prediction Hybrid Models for Aspects Extraction without Labelled Dataset Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism Team GPLSI. Approach for automated fact checking
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1