用强化学习生成电商广告标题，效果优于人类撰写的文案。
Ad Headline Generation using Self-Critical Masked Language Model
- 基于Transformer的掩码语言模型，结合强化学习策略梯度生成广告语。
- 在重叠率和质量评估中超越现有Transformer与LSTM+RL方法。
- 自动生成的标题在语法和创意性上均优于人工撰写，适合大规模电商应用。
对于任何电商平台而言，构建能持续吸引消费者的广告标题是一项艰巨挑战，尤其在大规模场景下更难达到网站的创意质量标准。为此，我们提出一种程序化解决方案，利用零售内容生成商品广告标题。该方法将基于Transformer的掩码语言模型与强化学习策略梯度方法相结合，通过联合条件建模多个待推广商品信息来生成广告语。实验表明，该方法在重叠率指标和质量审计中均优于现有的Transformer与LSTM+RL模型。此外，模型生成的标题在语法正确性和创意质量方面，经审计后表现优于人工提交的标题。
原文摘要 · Abstract (English)
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. 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. 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。