Multi-domain joint semantic frame parsing
AssigneeMicrosoft Technology Licensing, LLC
InventorsDilek Z Hakkani-Tur, Asli Celikyilmaz, Yun-Nung Chen, Li Deng, Jianfeng Gao, Gokhan Tur, Ye-Yi Wang
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AssigneeMicrosoft Technology Licensing, LLC
InventorsDilek Z Hakkani-Tur, Asli Celikyilmaz, Yun-Nung Chen, Li Deng, Jianfeng Gao, Gokhan Tur, Ye-Yi Wang
Estimating the term…
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.
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