Technical Q8A site answer recommendation via question boosting

• Main Authors: GAO, Zhipeng, XIA, Xin, LO, David, GRUNDY, John
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: CQA; Question Boosting; Question Answering; Sequence-to-sequence; Deep Neural Network; Weakly Supervised Learning; Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/6763; https://ink.library.smu.edu.sg/context/sis_research/article/7766/viewcontent/tosem205.pdf
• Institution: Singapore Management University

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.