{"title":"团队DOMLIN:为FEVER共享任务开发证据增强","authors":"Dominik Stammbach, G. Neumann","doi":"10.18653/v1/D19-6616","DOIUrl":null,"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":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task\",\"authors\":\"Dominik Stammbach, G. Neumann\",\"doi\":\"10.18653/v1/D19-6616\",\"DOIUrl\":null,\"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\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"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/D19-6616\",\"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/D19-6616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task
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.
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