论文标题
出生于自动标记：使用新的目标功能更快，更好
Born for Auto-Tagging: Faster and better with new objective functions
论文作者
论文摘要
关键字提取是文本挖掘的任务。它用于增加SEO和ADS中的搜索量。在自动标记中实现，它可以有效，准确地进行大规模的在线文章和照片的标签。 BAT是为AWOO的AI营销平台（AMP）发明的。 AWOO AMP不仅提供服务作为定制的推荐系统，而且还提高了电子商务中的转换率。蝙蝠的强度比其他SOTA模型更快，更好，因为其4层结构在50个时期达到了最佳的F分数。换句话说，它的性能要比其他需要在100个时代更深的层的模型更好。为了生成丰富和干净的标签，AWOO创建了新的目标功能，以保持相似的$ {\ rm f_1} $分数，同时增强$ {\ rm f_2} $同时得分。为了确保更好的f分数的表现，可以改善Transformer \ cite {Transformer}提出的学习率策略，以增加$ {\ rm f_1} $和$ {\ rm f_2} $得分。
Keyword extraction is a task of text mining. It is applied to increase search volume in SEO and ads. Implemented in auto-tagging, it makes tagging on a mass scale of online articles and photos efficiently and accurately. BAT is invented for auto-tagging which served as awoo's AI marketing platform (AMP). awoo AMP not only provides service as a customized recommender system but also increases the converting rate in E-commerce. The strength of BAT converges faster and better than other SOTA models, as its 4-layer structure achieves the best F scores at 50 epochs. In other words, it performs better than other models which require deeper layers at 100 epochs. To generate rich and clean tags, awoo creates new objective functions to maintain similar ${\rm F_1}$ scores with cross-entropy while enhancing ${\rm F_2}$ scores simultaneously. To assure the even better performance of F scores awoo revamps the learning rate strategy proposed by Transformer \cite{Transformer} to increase ${\rm F_1}$ and ${\rm F_2}$ scores at the same time.
