May 19, 2022, 1:11 a.m. | Penghui Wei, Xuanhua Yang, Shaoguo Liu, Liang Wang, Bo Zheng

cs.CL updates on arXiv.org arxiv.org

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 alleviate the low-resource issue, we …

advertising arxiv fine-tuning generation pre-training text text generation training

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Social Insights & Data Analyst (Freelance)

@ Media.Monks | Jakarta

Cloud Data Engineer

@ Arkatechture | Portland, ME, USA