Wei Fang, Moin Nadeem, Mitra Mohtarami, James R. Glass
{"title":"姿态预测的神经多任务学习","authors":"Wei Fang, Moin Nadeem, Mitra Mohtarami, James R. Glass","doi":"10.18653/v1/D19-6603","DOIUrl":null,"url":null,"abstract":"We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.","PeriodicalId":153447,"journal":{"name":"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Neural Multi-Task Learning for Stance Prediction\",\"authors\":\"Wei Fang, Moin Nadeem, Mitra Mohtarami, James R. Glass\",\"doi\":\"10.18653/v1/D19-6603\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.\",\"PeriodicalId\":153447,\"journal\":{\"name\":\"Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"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/D19-6603\",\"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/D19-6603","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.
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