  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.

Information Retrieval;Artificial Intelligence