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.

本文提出一种基于转移学习的对GitHub评论的对话行为分类方法，该方法能更好地理解虚拟团队在社交编码平台上的协作过程，采用多种编码模型进行比较，并探讨其在GitHub任务评论分类上的应用。

基于转移学习的GitHub问题评论对话行为分类方法