Invention Grant
- Patent Title: Learning with limited supervision for question-answering with light-weight Markov models
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Application No.: US16813098Application Date: 2020-03-09
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Publication No.: US12061995B2Publication Date: 2024-08-13
- Inventor: Trung Huu Bui , Tong Sun , Natwar Modani , Lidan Wang , Franck Dernoncourt
- Applicant: ADOBE INC.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Shook, Hardy & Bacon L.L.P.
- Main IPC: G06N7/01
- IPC: G06N7/01 ; G06F40/205 ; G06F40/279 ; G06F40/30 ; G06N20/00

Abstract:
Methods for natural language semantic matching performed by training and using a Markov Network model are provided. The trained Markov Network model can be used to identify answers to questions. Training may be performed using question-answer pairs that include labels indicating a correct or incorrect answer to a question. The trained Markov Network model can be used to identify answers to questions from sources stored on a database. The Markov Network model provides superior performance over other semantic matching models, in particular, where the training data set includes a different information domain type relative to the input question or the output answer of the trained Markov Network model.
Public/Granted literature
- US20210279622A1 LEARNING WITH LIMITED SUPERVISION FOR QUESTION-ANSWERING WITH LIGHT-WEIGHT MARKOV MODELS Public/Granted day:2021-09-09
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