论文标题
对话的转移学习方法Github的分类发行评论
A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments
论文作者
论文摘要
社会编码平台（例如GitHub）是研究开源软件开发中的协作问题解决方案的实验室；一个关键功能是他们支持发行报告的能力，该报告被团队使用来讨论任务和想法。如发表评论中所示，分析团队成员之间的对话可以产生有关虚拟团队表现的重要见解。本文提出了一种在发行评论上执行对话法的转移学习方法。由于没有大型标记的GitHub发行评论的标签，因此采用转移学习使我们能够与我们自己的GitHub评论数据集相结合利用标准对话ACT数据集。我们比较了几个单词和句子级别编码模型的性能，包括单词表示的全局向量（手套），通用句子编码器（使用）和来自变形金刚（Bert）的双向编码器表示。能够将问题评论映射到对话行为上是一个有用的垫脚石，用于理解认知团队过程。
Social coding platforms, such as GitHub, serve as laboratories for studying collaborative problem solving in open source software development; a key feature is their ability to support issue reporting which is used by teams to discuss tasks and ideas. Analyzing the dialogue between team members, as expressed in issue comments, can yield important insights about the performance of virtual teams. This paper presents a transfer learning approach for performing dialogue act classification on issue comments. Since no large labeled corpus of GitHub issue comments exists, employing transfer learning enables us to leverage standard dialogue act datasets in combination with our own GitHub comment dataset. We compare the performance of several word and sentence level encoding models including Global Vectors for Word Representations (GloVe), Universal Sentence Encoder (USE), and Bidirectional Encoder Representations from Transformers (BERT). Being able to map the issue comments to dialogue acts is a useful stepping stone towards understanding cognitive team processes.
