Microsoft Technology Licensing, LLC

Ye Yi Wang

Jin Li

Li Deng

Jianfeng Gao

Gokhan Tur

Cheng Huang

Asli Celikyilmaz

Richard R Yang

Yun-Nung Chen

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 (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.

Multi-domain joint semantic frame parsing

Techniques and technologies for routing communications based on Quality of Service (QOS) related information. More particularly, this document discloses techniques and technologies for selecting communications paths which partially overlap other communication paths for which QOS related information has been measured. The techniques and technologies include determining, performance levels for path segments within the communication paths from the measured QOS information.

Quality of service (QOS) based systems, networks, and advisors

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Redmond, WA, United States of America

Ye Yi Wang