We present DeepGen, a system deployed at web scale for automatically creating sponsored search advertisements (ads) for BingAds customers. We leverage state-of-the-art natural language generation (NLG) models to generate fluent ads from advertiser's web pages in an abstractive fashion and solve practical issues such as factuality and inference speed. In addition, our system creates a customized ad in real-time in response to the user's search query, therefore highlighting different aspects of the same product based on what the user is looking for. To achieve this, our system generates a diverse choice of smaller pieces of the ad ahead of time and, at query time, selects the most relevant ones to be stitched into a complete ad. We improve generation diversity by training a controllable NLG model to generate multiple ads for the same web page highlighting different selling points. Our system design further improves diversity horizontally by first running an ensemble of generation models trained with different objectives and then using a diversity sampling algorithm to pick a diverse subset of generation results for online selection. Experimental results show the effectiveness of our proposed system design. Our system is currently deployed in production, serving ${\sim}4\%$ of global ads served in Bing.
翻译:我们以网络规模推出DeepGen, 用于自动为BingAds客户创建赞助搜索广告(ads)的系统DeepGen。 我们利用最先进的自然语言生成模型(NLG),从广告商的网页上以抽象的方式生成流畅的广告,解决事实品质和推断速度等实际问题。 此外, 我们的系统根据用户的搜索查询,实时制作一个定制广告, 从而根据用户正在寻找的目标, 突出同一产品的不同方面。 为了实现这一点, 我们的系统生成了对更小的广告的多种选择, 并在查询时选择了最相关的广告。 我们通过培训一个可控制的NLG模型, 为同一网页制作多个广告, 突出不同的销售点, 来改进下一代的多样性。 我们的系统设计通过首先运行一组经过不同目标培训的生成模型, 然后使用多样性的抽样算法, 来为在线选择不同版本的结果选择一个子组。 实验结果显示我们目前投入全球生产的系统设计 $ 。