Ad Headline Generation using Self-Critical Masked Language Model

使用自评掩码语言模型生成广告标题

Abstract: For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers. It is hard to pass the creative quality bar of the website, especially at a large scale. We thus propose a programmatic solution to generate product advertising headlines using retail content.

摘要： 对于任何电子商务网站而言，制作能够吸引消费者的持久性广告都是一个不小的难题。尤其是在大规模应用的情况下，要达到网站的创意质量标准非常困难。因此，我们提出了一种程序化解决方案，利用零售内容来生成产品广告标题。

We propose a state of the art application of Reinforcement Learning (RL) Policy gradient methods on Transformer based Masked Language Models. Our method creates the advertising headline by jointly conditioning on multiple products that a seller wishes to advertise.

我们提出了一种将强化学习（RL）策略梯度方法应用于基于 Transformer 的掩码语言模型的前沿应用。我们的方法通过联合调节卖家希望推广的多种产品来创建广告标题。

We demonstrate that our method outperforms existing Transformer and LSTM + RL methods in overlap metrics and quality audits. We also show that our model-generated headlines outperform human submitted headlines in terms of both grammar and creative quality as determined by audits.

我们证明了该方法在重叠指标和质量审核方面优于现有的 Transformer 和 LSTM + RL 方法。我们还表明，经审核认定，我们的模型生成的标题在语法和创意质量方面均优于人工提交的标题。