The core challenge in numerous real-world applications is to match an inquiry to the best document from a mutable and finite set of candidates. Existing industry solutions, especially latency-constrained services, often rely on similarity algorithms that sacrifice quality for speed. In this paper we introduce a generic semantic learning-to-rank framework, Self-training Semantic Cross-attention Ranking (sRank). This transformer-based framework uses linear pairwise loss with mutable training batch sizes and achieves quality gains and high efficiency, and has been applied effectively to show gains on two industry tasks at Microsoft over real-world large-scale data sets: Smart Reply (SR) and Ambient Clinical Intelligence (ACI). In Smart Reply, $sRank$ assists live customers with technical support by selecting the best reply from predefined solutions based on consumer and support agent messages. It achieves 11.7% gain in offline top-one accuracy on the SR task over the previous system, and has enabled 38.7% time reduction in composing messages in telemetry recorded since its general release in January 2021. In the ACI task, sRank selects relevant historical physician templates that serve as guidance for a text summarization model to generate higher quality medical notes. It achieves 35.5% top-one accuracy gain, along with 46% relative ROUGE-L gain in generated medical notes.

翻译：众多现实应用的核心挑战在于从一组可变且有限候选项中，为查询匹配最佳文档。现有行业解决方案（尤其是延迟受限服务）通常依赖牺牲质量换取速度的相似度算法。本文提出一种通用的语义学习排序框架——自训练语义交叉注意力排序（sRank）。该基于Transformer的框架采用可变的训练批量大小与线性成对损失函数，在实现质量提升的同时保持高计算效率，并已成功应用于微软两大真实大规模数据集上的工业任务：智能回复（SR）与环境临床智能（ACI）。在智能回复任务中，sRank通过分析消费者与客服代表的对话消息，从预定义解决方案中选取最佳回复，为线上客户提供技术支持。相比原有系统，该框架在SR任务的离线Top-1准确率上取得11.7%的提升，且自2021年1月全面发布以来的遥测数据显示，用户编写消息的时间减少了38.7%。在ACI任务中，sRank选取相关历史医生模板作为文本摘要模型的指导，从而生成更高质量的医疗记录。该框架在Top-1准确率上获得35.5%的提升，同时使生成的医疗记录相对ROUGE-L指标提升46%。