Technical Q&A Site Answer Recommendation via Question Boosting

TL;DR

This paper introduces DeepAns, a neural network-based method that improves answer recommendation on technical Q&A sites by generating clarifying questions and ranking answers, significantly reducing unanswered questions.

Contribution

The paper presents a novel three-stage approach combining question boosting, label establishment, and answer ranking using neural networks for better answer recommendation.

Findings

DeepAns outperforms state-of-the-art baselines in automatic evaluation.

The approach effectively recommends relevant answers, reducing unanswered questions.

User study confirms improved answer relevance and resolution rate.

Abstract

Software developers have heavily used online question and answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q&A sites is "answer hungriness" i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers with various levels of quality. To alleviate this time-consuming problem, we propose a novel DeepAns neural network-based approach to identify the most relevant answer among a set of answer candidates. Our approach follows a three-stage process: question boosting, label establishment, and answer recommendation. Given a post, we first generate a clarifying question as a way of question boosting. We automatically establish the positive, neutral+, neutral- and negative training samples via label establishment. When it comes to answer recommendation, we sort answer candidates by the matching scores calculated by our neural network-based model. To evaluate the performance of our proposed model, we conducted a large scale evaluation on four datasets, collected from the real world technical Q&A sites (i.e., Ask Ubuntu, Super User, Stack Overflow Python and Stack Overflow Java). Our experimental results show that our approach significantly outperforms several state-of-the-art baselines in automatic evaluation. We also conducted a user study with 50 solved/unanswered/unresolved questions. The user study results demonstrate that our approach is effective in solving the answer hungry problem by recommending the most relevant answers from historical archives.