Online advertising is an important revenue source for many IT companies. In the search advertising scenario, advertisement text that meets the need of the search query would be more attractive to the user. However, the manual creation of query-variant advertisement texts for massive items is expensive. Traditional text generation methods tend to focus on the general searching needs with high frequency while ignoring the diverse personalized searching needs with low frequency. In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries with various needs based on queries and item keywords. To solve the problem of ignoring low-frequency needs, we propose a dynamic association mechanism to expand the receptive field based on external knowledge, which can obtain associated words to be added to the input. These associated words can serve as bridges to transfer the ability of the model from the familiar high-frequency words to the unfamiliar low-frequency words. With association, the model can make use of various personalized needs in queries and generate query-variant advertisement texts. Both automatic and human evaluations show that our model can generate more attractive advertisement text than baselines.
翻译:在线广告是许多信息技术公司的一个重要收入来源。在搜索广告的情景中,满足搜索查询需要的广告文本对用户来说更具吸引力。然而,为大型项目手工制作问答式广告文本的费用昂贵。传统的文本生成方法往往侧重于一般搜索需求,高频率,而忽视个人个人化搜索需求,低频率。在本文中,我们提议通过问答生成文本来为不同网络搜索查询生成候选广告文本,这些版本基于查询和项目关键词的不同需求。为解决忽视低频率需求的问题,我们提议一个动态的关联机制,以外部知识为基础扩大可接收的字段,这可以获取相关词来加入输入。这些相关词可以作为桥梁,将模型的能力从熟悉的高频字转换到不熟悉的低频字眼。通过连带,模型可以在查询中使用各种个人化需求并生成问答式广告文本。自动和人文评估都表明,我们的模型能够产生比基线更具吸引力的广告文本。