Multi-domain joint semantic frame parsing

US11783173B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Dilek Hakkani-Tur · Los Altos, US
• Asli Celikyilmaz · Mountain View, US
• Yun-Nung Chen · Bellevue, US
• Li Deng · Redmond, US
• Jianfeng Gao · Woodinville, US
• Gokhan Tur · Morristown, US
• Ye-Yi Wang · Redmond, US

Key dates

• Filing date: Aug 4, 2016
• Grant date: Oct 10, 2023
• Expiry date: Feb 27, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G10L15/22
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A processing unit can train a model as a joint multi-domain recurrent neural network (JRNN), such as a bi-directional recurrent neural network (bRNN) and/or a recurrent neural network with long-short term memory (RNN-LSTM) for spoken language understanding (SLU). The processing unit can use the trained model to, e.g., jointly model slot filling, intent determination, and domain classification. The joint multi-domain model described herein can estimate a complete semantic frame per query, and the joint multi-domain model enables multi-task deep learning leveraging the data from multiple domains. The joint multi-domain recurrent neural network (JRNN) can leverage semantic intents (such as, finding or identifying, e.g., a domain specific goal) and slots (such as, dates, times, locations, subjects, etc.) across multiple domains.