Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models (1905.01966v2)

Abstract: Domain-specific community question answering is becoming an integral part of professions. Finding related questions and answers in these communities can significantly improve the effectiveness and efficiency of information seeking. Stack Overflow is one of the most popular communities that is being used by millions of programmers. In this paper, we analyze the problem of predicting knowledge unit (question thread) relatedness in Stack Overflow. In particular, we formulate the question relatedness task as a multi-class classification problem with four degrees of relatedness. We present a large-scale dataset with more than 300K pairs. To the best of our knowledge, this dataset is the largest domain-specific dataset for Question-Question relatedness. We present the steps that we took to collect, clean, process, and assure the quality of the dataset. The proposed dataset Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the prediction of relatedness in technical community question-answering forums. We adopt a neural network architecture and a traditional model for this task that effectively utilize information from different parts of knowledge units to compute the relatedness between them. These models can be used to benchmark novel models, as they perform well in our task and in a closely similar task.