The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short, and there are just too many to remember. In such a case, the users may want to ask contextual queries that describe the features they are looking for, but the standard term frequency-based search cannot process them. This paper presents a novel retrieval system for mobile features that accepts intuitive and contextual search queries. We trained a relevance model via contrastive learning from a pre-trained language model to perceive the contextual relevance between query embeddings and indexed mobile features. Also, to make it run efficiently on-device using minimal resources, we applied knowledge distillation to compress the model without degrading much performance. To verify the feasibility of our method, we collected test queries and conducted comparative experiments with the currently deployed search baselines. The results show that our system outperforms the others on contextual sentence queries and even on usual keyword-based queries.

本研究提出了一种接受直观且有背景的搜索查询的移动特性检索系统，通过对预训练语言模型进行对比学习，训练相关模型以感知查询嵌入和索引移动特性之间的上下文相关性，并通过知识蒸馏压缩模型以在设备上高效运行。通过与当前部署的搜索基准进行比较实验证实了我们方法的可行性，结果显示我们的系统在上下文句子查询和基于关键词的查询上均优于其他系统。

通过对比学习训练的相关模型实现对智能手机设置的直观访问