US 2021/0279622 A1 · LEARNING WITH LIMITED SUPERVISION FOR QUESTION-ANSWERING WITH LIGHT-WEIGHT MARKOV MODELS

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.

Claims20

Dependent claims

One or more computer storage media having computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to train a Markov Network model for identifying answers to questions by performing operations comprising:

receiving training data comprising question-answer pairs;
identifying object texts and relation texts within the question-answer pairs;
generating a Markov Network comprising a node network of object nodes and relation nodes, wherein the object nodes are based on the identified object texts and the relation nodes are based on the identified relation texts;
training the Markov Network by optimizing a feature function associated with the Markov Network, the training resulting in a trained Markov Network model;
and
storing the trained Markov Network model for use in identifying an answer to a question.

3 more elements

A computerized method identifying answers to questions using a trained Markov Network model for identifying answers to questions, the method comprising:

receiving a question from a user computing device;
identifying an answer to the question using a trained Markov Network model, the trained Markov Network model determined by training a Markov Network comprising a node network of object nodes and relation nodes, the object nodes based on object texts and the relation nodes based on relation texts identified from question-answer pairs of training data;
and
providing the answer to the user computing device in response to receiving the question.

1 more element

Dependencies are inferred from the claim text.

Learning with limited supervision for question-answering with light-weight markov models

Life of this patent

Learning with limited supervision for question-answering with light-weight markov models

Life of this patent

Abstract

Claims20