Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning

Autor:	Kim, Joonyoung, Lee, Kangwook, Shin, Haebin, Lee, Hurnjoo, Kang, Sechun, Choi, Byunguk, Shin, Dong, Lee, Joohyung
Rok vydání:	2023
Předmět:	Computer Science - Information Retrieval Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, IAAI 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.09177 Zobrazit plný text záznamu View this record from Arxiv