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 Q8A 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 D EEP A NS 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 Q8A 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.

中文翻译：

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通过问题提升的技术 Q8A 站点答案推荐

软件开发人员大量使用在线问答平台来寻求帮助以解决他们的技术问题。然而，这些技术 Q8A 站点的一个主要问题是“回答饥饿，”即，大量问题仍未得到解答或未解决，用户不得不等待很长时间或费力地浏览所提供的各种质量级别的答案。为了缓解这个耗时的问题，我们提出了一种新颖的 DEEP一种NS基于神经网络的方法来识别一组候选答案中最相关的答案。我们的方法遵循三个阶段的过程：问题提升、标签建立和答案推荐。给定一个帖子，我们首先生成一个澄清问题作为问题提升的一种方式。我们自动建立积极的,中性的+ ,中性的- ， 和消极的通过标签建立训练样本。在回答推荐方面，我们根据基于神经网络的模型计算的匹配分数对候选答案进行排序。为了评估我们提出的模型的性能，我们对四个数据集进行了大规模评估，这些数据集是从现实世界的技术 Q8A 站点（即 Ask Ubuntu、Super User、Stack Overflow Python 和 Stack Overflow Java）收集的。我们的实验结果表明，我们的方法在自动评估中明显优于几个最先进的基线。我们还对 50 个已解决/未回答/未解决的问题进行了用户研究。用户研究结果表明，我们的方法通过从历史档案中推荐最相关的答案来有效地解决答案渴望问题。