In community question-answering platforms, tags play essential roles in effective information organization and retrieval, better question routing, faster response to questions, and assessment of topic popularity. Hence, automatic assistance for predicting and suggesting tags for posts is of high utility to users of such platforms. To develop better tag prediction across diverse communities and domains, we performed a thorough analysis of users' tagging behavior in 17 StackExchange communities. We found various common inherent properties of this behavior in those diverse domains. We used the findings to develop a flexible neural tag prediction architecture, which predicts both popular tags and more granular tags for each question. Our extensive experiments and obtained performance show the effectiveness of our model

翻译：在社区问答平台中，标签在有效信息组织与检索、问题精准路由、快速响应以及热点话题评估中发挥着关键作用。因此，自动预测并推荐标签的功能对此类平台用户具有重要实用价值。为开发适用于不同社区和领域的通用型标签预测方法，我们对17个StackExchange社区的用户标注行为进行了深入分析，发现了这些多元领域中共有的行为特性。基于研究结果，我们构建了灵活的神经标签预测架构，该架构能对每个问题同时预测热门标签与精细化标签。大量实验及性能表现验证了我们模型的有效性。