A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments
This work addresses the challenge of analyzing team collaboration in open-source software development, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of classifying dialogue acts in GitHub issue comments by using transfer learning to leverage existing dialogue act datasets due to the lack of large labeled GitHub data, and found that models like BERT performed well in this task.
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.