CREATER: ctrl驱动的广告文本生成与控制的预训练和对比微调

Penghui Wei, Xuanhua Yang, Shaoguo Liu, Liang Wang, Bo Zheng
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引用次数: 7

摘要

本文的重点是自动生成广告文本,目标是生成的文本可以捕获用户的兴趣,以实现更高的点击率(CTR)。我们提出了一种名为CREATER的广告文本生成方法,它基于高质量的用户评论生成广告文本。为了结合点击率目标,我们的模型通过对比学习从在线A/B测试数据中学习,这鼓励模型生成获得更高点击率的广告文本。为了利用大规模的非配对评论,我们设计了一个定制的自监督目标,减少了预训练和微调之间的差距。在工业数据集上的实验表明,CREATER显著优于当前的方法。它已被部署在一个领先的在线广告平台上,并带来了核心在线指标的提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). We propose CREATER, a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews. To incorporate CTR objective, our model learns from online A/B test data with contrastive learning, which encourages the model to generate ad texts that obtain higher CTR. To make use of large-scale unpaired reviews, we design a customized self-supervised objective reducing the gap between pre-training and fine-tuning. Experiments on industrial datasets show that CREATER significantly outperforms current approaches. It has been deployed online in a leading advertising platform and brings uplift on core online metrics.
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