- Yun-Nung, Ming Sun, Alexander I. Rudnicky, A. Gershman. 2015. Matrix Factorization with Domain Knowledge and Behavioral Patterns for Intent Modeling. Abstract: Spoken language interfaces are being incorporated into various devices such as smart-phones and TVs. However, dialog systems will fail to respond correctly when users request functionality not supported by currently installed apps. We propose a feature-enriched matrix factorization (MF) approach to model open domain intents that allow a system to dynamically add app-relevant domains according to users’ requests. We use MF to jointly model published app descriptions and users’ spoken requests; this generates latent feature vectors for utterances and user intents without need for prior annotation. The matrix can further incorporate user behavioral patterns found in their activity logs to learn user-specific intent prediction models. We show that the MF models enriched with multimodality significantly improve the intent prediction, achieving 34% and 55% of mean average precision (MAP) for unsupervised single-turn requests and for supervised multi-turn interactions on ASR transcripts respectively.
