使用预训练的语言模型发布报告分类

2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE) Pub Date : 2022-05-01 DOI:10.1145/3528588.3528659

Giuseppe Colavito, F. Lanubile, Nicole Novielli

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引用次数: 7

摘要

本文描述了我们在第一届基于自然语言的软件工程国际研讨会范围内组织的工具竞赛中的参与情况。我们提出了一种基于bert的语言模型的监督方法，用于GitHub问题的自动分类。我们对不同的预训练模型进行了实验，通过微调RoBERTa获得了最佳性能(F1 = .8591)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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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来源期刊

2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)

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