Self-Supervised Augmentation and Generation for Multi-lingual Text Advertisements at Bing

Multi-lingual text advertisement generation is a critical task for international companies, such as Microsoft. Due to the lack of training data, scaling out text advertisements generation to low-resource languages is a grand challenge in the real industry setting. Although some methods transfer knowledge from rich-resource languages to low-resource languages through a pre-trained multi-lingual language model, they fail in balancing the transferability from the source language and the smooth expression in target languages. In this paper, we propose a unified Self-Supervised Augmentation and Generation (SAG) architecture to handle the multi-lingual text advertisements generation task in a real production scenario. To alleviate the problem of data scarcity, we employ multiple data augmentation strategies to synthesize training data in target languages. Moreover, a self-supervised adaptive filtering structure is developed to alleviate the impact of the noise in the augmented data. The new state-of-the-art results on a well-known benchmark verify the effectiveness and generalizability of our proposed framework, and deployment in Microsoft Bing demonstrates the superior performance of our method.