CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
Penghui Wei, Xuanhua Yang, ShaoGuo Liu, Liang Wang, Bo Zheng
Abstract
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.
BibTeX
@inproceedings{wei-etal-2022-creater,
title = "{CREATER}: {CTR}-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning",
author = "Wei, Penghui and
Yang, Xuanhua and
Liu, ShaoGuo and
Wang, Liang and
Zheng, Bo",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.2/",
doi = "10.18653/v1/2022.naacl-industry.2",
pages = "9--17"
}