{"title":"使用预训练的语言模型发布报告分类","authors":"Giuseppe Colavito, F. Lanubile, Nicole Novielli","doi":"10.1145/3528588.3528659","DOIUrl":null,"url":null,"abstract":"This paper describes our participation in the tool competition organized in the scope of the 1st International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on fine-tuned BERT-based language models for the automatic classification of GitHub issues. We experimented with different pre-trained models, achieving the best performance with fine-tuned RoBERTa (F1 = .8591).","PeriodicalId":313397,"journal":{"name":"2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)","volume":"3 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Issue Report Classification Using Pre-trained Language Models\",\"authors\":\"Giuseppe Colavito, F. Lanubile, Nicole Novielli\",\"doi\":\"10.1145/3528588.3528659\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes our participation in the tool competition organized in the scope of the 1st International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on fine-tuned BERT-based language models for the automatic classification of GitHub issues. We experimented with different pre-trained models, achieving the best performance with fine-tuned RoBERTa (F1 = .8591).\",\"PeriodicalId\":313397,\"journal\":{\"name\":\"2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)\",\"volume\":\"3 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3528588.3528659\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3528588.3528659","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Issue Report Classification Using Pre-trained Language Models
This paper describes our participation in the tool competition organized in the scope of the 1st International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on fine-tuned BERT-based language models for the automatic classification of GitHub issues. We experimented with different pre-trained models, achieving the best performance with fine-tuned RoBERTa (F1 = .8591).
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