  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.
