Invention Grant
- Patent Title: System and method for cross-domain transferable neural coherence model
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Application No.: US16669741Application Date: 2019-10-31
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Publication No.: US11270072B2Publication Date: 2022-03-08
- Inventor: Yanshuai Cao , Peng Z. Xu , Hamidreza Saghir , Jin Sung Kang , Teng Long , Jackie C. K. Cheung
- Applicant: ROYAL BANK OF CANADA
- Applicant Address: CA Toronto
- Assignee: ROYAL BANK OF CANADA
- Current Assignee: ROYAL BANK OF CANADA
- Current Assignee Address: CA Toronto
- Agency: Norton Rose Fulbright Canada LLP
- Main IPC: G06F40/284
- IPC: G06F40/284 ; G06N3/08 ; G06N3/04 ; G06F40/30

Abstract:
Systems and methods of automatically generating a coherence score for text data is provided. The approach includes receiving a plurality of string tokens representing decomposed portions of the target text data object. A trained neural network is provided that has been trained against a plurality of corpuses of training text across a plurality of topics. The string tokens are arranged to extract string tokens representing adjacent sentence pairs of the target text data object. For each adjacent sentence pair, the neural network generates a local coherence score representing a coherence level of the adjacent sentence pair of the target text data object, which are then aggregated for each adjacent sentence pair of the target text data object to generate a global coherence score for the target text data object.
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