Attention-based model for predicting question relatedness on Stack Overflow

TL;DR

This paper introduces an attention-based deep learning model, ASIM, for predicting question relatedness on Stack Overflow, leveraging semantic interaction capture and domain-specific embeddings to outperform existing methods.

Contribution

The paper proposes a novel attention-based sentence pair interaction model with domain-specific embeddings, achieving state-of-the-art results in question relatedness prediction.

Findings

ASIM outperforms baseline models in Precision, Recall, and Micro-F1.

The model generalizes well to duplicate question detection on AskUbuntu.

Pre-trained domain-specific embeddings improve prediction accuracy.

Abstract

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.