Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models
This work addresses the challenge of improving information retrieval for programmers on technical forums like Stack Overflow, though it is incremental as it builds on existing methods with a new dataset.
The authors tackled the problem of predicting question relatedness on Stack Overflow by formulating it as a multi-class classification task and created a large-scale dataset with over 300K pairs, which is the largest domain-specific dataset for this purpose, and developed neural network and traditional models that perform well for benchmarking.
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.