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Social coding platforms, such as GitHub, serve as laboratories for studyingcollaborative problem solving in open source software development; a keyfeature is their ability to support issue reporting which is used by teams todiscuss tasks and ideas. Analyzing the dialogue between team members, asexpressed in issue comments, can yield important insights about the performanceof virtual teams. This paper presents a transfer learning approach forperforming dialogue act classification on issue comments. Since no largelabeled corpus of GitHub issue comments exists, employing transfer learningenables us to leverage standard dialogue act datasets in combination with ourown GitHub comment dataset. We compare the performance of several word andsentence level encoding models including Global Vectors for WordRepresentations (GloVe), Universal Sentence Encoder (USE), and BidirectionalEncoder Representations from Transformers (BERT). Being able to map the issuecomments to dialogue acts is a useful stepping stone towards understandingcognitive team processes.

A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments