Online Q&A and open source communities use tags and keywords to index, categorize, and search for specific content. The most obvious advantage of tag recommendation is the correct classification of information. In this study, we used the BERT pre-training technique in tag recommendation task for online Q&A and open-source communities for the first time. Our evaluation on freecode datasets show that the proposed method, called TagBERT, is more accurate compared to deep learning and other baseline methods. Moreover, our model achieved a high stability by solving the problem of previous researches, where increasing the number of tag recommendations significantly reduced model performance.

翻译：在线 ⁇ A 和 开放源码 社群使用标签和关键字来索引、分类和搜索特定内容。 标签建议的最明显优点是正确的信息分类。 在本研究中,我们首次使用BERT 预培训技术来给在线 ⁇ A 和 开放源代码社群做标签建议任务。 我们对自由码数据集的评估表明,与深层学习和其他基线方法相比,拟议方法(称为 TagBERT)更准确。 此外,我们的模型通过解决先前研究的问题实现了高度稳定,因为以前研究的问题使标签建议数量增加大大降低了模型性能。