  Accurately predicting conversions in advertisements is generally a
challenging task, because such conversions do not occur frequently. In this
paper, we propose a new framework to support creating high-performing ad
creatives, including the accurate prediction of ad creative text conversions
before delivering to the consumer. The proposed framework includes three key
ideas: multi-task learning, conditional attention, and attention highlighting.
Multi-task learning is an idea for improving the prediction accuracy of
conversion, which predicts clicks and conversions simultaneously, to solve the
difficulty of data imbalance. Furthermore, conditional attention focuses
attention of each ad creative with the consideration of its genre and target
gender, thus improving conversion prediction accuracy. Attention highlighting
visualizes important words and/or phrases based on conditional attention. We
evaluated the proposed framework with actual delivery history data (14,000
creatives displayed more than a certain number of times from Gunosy Inc.), and
confirmed that these ideas improve the prediction performance of conversions,
and visualize noteworthy words according to the creatives' attributes.
