Invention Grant
- Patent Title: Semantic parsing using deep neural networks for predicting canonical forms
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Application No.: US15147222Application Date: 2016-05-05
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Publication No.: US09858263B2Publication Date: 2018-01-02
- Inventor: Chunyang Xiao , Marc Dymetman , Claire Gardent
- Applicant: Conduent Business Services, LLC
- Applicant Address: US TX Dallas FR Paris
- Assignee: Conduent Business Services, LLC,Centre National De La Recherche Scientifique
- Current Assignee: Conduent Business Services, LLC,Centre National De La Recherche Scientifique
- Current Assignee Address: US TX Dallas FR Paris
- Agency: Fay Sharpe LLP
- Main IPC: G06F17/27
- IPC: G06F17/27 ; G10L15/16 ; G10L19/00 ; G10L15/197 ; G06F17/30 ; G06N3/08

Abstract:
A method for predicting a canonical form for an input text sequence includes predicting the canonical form with a neural network model. The model includes an encoder, which generates a first representation of the input text sequence based on a representation of n-grams in the text sequence and a second representation of the input text sequence generated by a first neural network. The model also includes a decoder which sequentially predicts terms of the canonical form based on the first and second representations and a predicted prefix of the canonical form. The canonical form can be used, for example, to query a knowledge base or to generate a next utterance in a discourse.
Public/Granted literature
- US20170323636A1 SEMANTIC PARSING USING DEEP NEURAL NETWORKS FOR PREDICTING CANONICAL FORMS Public/Granted day:2017-11-09
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