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Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)最新文献

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Improving Evidence Detection by Leveraging Warrants 利用搜查令改进证据侦查
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 1900-01-01 DOI: 10.18653/v1/D19-6610
Keshav Singh, Paul Reisert, Naoya Inoue, Pride Kavumba, Kentaro Inui
{"title":"Improving Evidence Detection by Leveraging Warrants","authors":"Keshav Singh, Paul Reisert, Naoya Inoue, Pride Kavumba, Kentaro Inui","doi":"10.18653/v1/D19-6610","DOIUrl":"https://doi.org/10.18653/v1/D19-6610","url":null,"abstract":"Recognizing the implicit link between a claim and a piece of evidence (i.e. warrant) is the key to improving the performance of evidence detection. In this work, we explore the effectiveness of automatically extracted warrants for evidence detection. Given a claim and candidate evidence, our proposed method extracts multiple warrants via similarity search from an existing, structured corpus of arguments. We then attentively aggregate the extracted warrants, considering the consistency between the given argument and the acquired warrants. Although a qualitative analysis on the warrants shows that the extraction method needs to be improved, our results indicate that our method can still improve the performance of evidence detection.","PeriodicalId":153447,"journal":{"name":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127309043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task 团队DOMLIN:为FEVER共享任务开发证据增强
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 1900-01-01 DOI: 10.18653/v1/D19-6616
Dominik Stammbach, G. Neumann
{"title":"Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task","authors":"Dominik Stammbach, G. Neumann","doi":"10.18653/v1/D19-6616","DOIUrl":"https://doi.org/10.18653/v1/D19-6616","url":null,"abstract":"This paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge. We propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is not only conditioned on the claim, but also on previously retrieved evidence. We use a publicly available document retrieval module and have fine-tuned BERT checkpoints for sentence se- lection and as the entailment classifier. We report a FEVER score of 68.46% on the blind testset.","PeriodicalId":153447,"journal":{"name":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133579096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 32
Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification 提取与聚合:一种新的领域无关的事实数据验证方法
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER) Pub Date : 1900-01-01 DOI: 10.18653/v1/D19-6612
Anton Chernyavskiy, Dmitry I. Ilvovsky
{"title":"Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification","authors":"Anton Chernyavskiy, Dmitry I. Ilvovsky","doi":"10.18653/v1/D19-6612","DOIUrl":"https://doi.org/10.18653/v1/D19-6612","url":null,"abstract":"Triggered by Internet development, a large amount of information is published in online sources. However, it is a well-known fact that publications are inundated with inaccurate data. That is why fact-checking has become a significant topic in the last 5 years. It is widely accepted that factual data verification is a challenge even for the experts. This paper presents a domain-independent fact checking system. It can solve the fact verification problem entirely or at the individual stages. The proposed model combines various advanced methods of text data analysis, such as BERT and Infersent. The theoretical and empirical study of the system features is carried out. Based on FEVER and Fact Checking Challenge test-collections, experimental results demonstrate that our model can achieve the score on a par with state-of-the-art models designed by the specificity of particular datasets.","PeriodicalId":153447,"journal":{"name":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121411642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
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