Advertising is an important revenue source for many companies. However, it is expensive to manually create advertisements that meet the needs of various queries for massive items. In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisements for different queries with various needs given the item keywords. In this task, for many different queries there is only one general purposed advertisement with no predefined query-advertisement pair, which would discourage traditional End-to-End models from generating query-variant advertisements for different queries with different needs. To deal with the problem, we propose a query-variant advertisement text generation model that takes keywords and associated external knowledge as input during training and adds different queries during inference. Adding external knowledge helps the model adapted to the information besides the item keywords during training, which makes the transition between training and inference more smoothing when the query is added during inference. Both automatic and human evaluation show that our model can generate more attractive and query-focused advertisements than the strong baselines.
翻译:广告是许多公司的一个重要收入来源。 然而, 手工创建满足各种大项目查询需要的广告成本很高 。 在本文中, 我们提出询问变式广告文本生成任务, 目的是为不同询问生成符合项目关键字的不同需求的候选人广告 。 在此项任务中, 对于许多不同的询问, 只有一个通用广告, 没有预先定义的查询广告配对, 这会阻止传统的端对端模式为不同需求的不同询问生成查询变换广告 。 为了解决这个问题, 我们提议了一个查询变式广告文本生成模式, 将关键词和相关外部知识作为培训的投入, 并在推断过程中添加不同的查询。 添加外部知识有助于模型适应培训中项目关键字之外的信息, 这使得在推断过程中添加询问时, 培训与引言之间的过渡更加顺利。 自动和人文评估都表明, 我们的模型能够产生比强健的基线更具吸引力和以询问为焦点的广告。