{"title":"使用维基百科编辑在低资源语法错误纠正","authors":"Adriane Boyd","doi":"10.18653/v1/W18-6111","DOIUrl":null,"url":null,"abstract":"We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5%.","PeriodicalId":207795,"journal":{"name":"NUT@EMNLP","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"51","resultStr":"{\"title\":\"Using Wikipedia Edits in Low Resource Grammatical Error Correction\",\"authors\":\"Adriane Boyd\",\"doi\":\"10.18653/v1/W18-6111\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5%.\",\"PeriodicalId\":207795,\"journal\":{\"name\":\"NUT@EMNLP\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"51\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NUT@EMNLP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/W18-6111\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NUT@EMNLP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W18-6111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 51
摘要
我们开发了一个德语语法错误纠正(GEC)系统,使用一个小的黄金GEC语料库,增强了从维基百科修订历史中提取的编辑。我们扩展了自动错误注释工具ERRANT (Bryant et al., 2017)的德语,并使用它来分析黄金GEC更正和维基百科编辑(Grundkiewicz和junczys - dowmont, 2014),以选择包含类似于黄金语料库中语法更正的维基百科编辑作为额外的训练数据。使用多层卷积编码器-解码器神经网络GEC方法(Chollampatt和Ng, 2018),我们评估了维基百科编辑的贡献,发现精心选择的维基百科编辑将性能提高了5%以上。
Using Wikipedia Edits in Low Resource Grammatical Error Correction
We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5%.