  Stack Overflow is one of the most popular Programming Community-based
Question Answering (PCQA) websites that has attracted more and more users in
recent years. When users raise or inquire questions in Stack Overflow,
providing related questions can help them solve problems. Although there are
many approaches based on deep learning that can automatically predict the
relatedness between questions, those approaches are limited since interaction
information between two questions may be lost. In this paper, we adopt the deep
learning technique, propose an Attention-based Sentence pair Interaction Model
(ASIM) to predict the relatedness between questions on Stack Overflow
automatically. We adopt the attention mechanism to capture the semantic
interaction information between the questions. Besides, we have pre-trained and
released word embeddings specific to the software engineering domain for this
task, which may also help other related tasks. The experiment results
demonstrate that ASIM has made significant improvement over the baseline
approaches in Precision, Recall, and Micro-F1 evaluation metrics, achieving
state-of-the-art performance in this task. Our model also performs well in the
duplicate question detection task of AskUbuntu, which is a similar but
different task, proving its generalization and robustness.
