Method and apparatus for generating natural language
AssigneeSamsung Electronics Co., Ltd.
InventorsJunhwi CHOI, Young-Seok KIM, Sang Hyun YOO, Jehun JEON
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Abstract
A natural language generation method and apparatus are provided. The natural language generation apparatus converts an input sentence to a first vector using a first neural network model-based encoder, determines whether a control word is to be provided based on a criterion, and converts the first vector to an output sentence using a neural network model-based decoder, based on whether the control word is to be provided.
Claims21
A method of generating a natural language, the method comprising:
- converting an input sentence to a first vector using a first neural network model-based encoder;
- determining whether a control word is to be provided based on a condition;
- and
- converting the first vector to an output sentence using a neural network model-based decoder based on whether the control word is to be provided.
A method of training a natural language generation apparatus, the method comprising:
- converting a training sentence to a first vector using a first neural network model-based encoder;
- determining whether a control word is to be provided based on a criterion;
- converting the first vector to an output sentence using a neural network model-based decoder based on whether the control word is to be provided;
- and
- training the first encoder and the decoder by evaluating an accuracy of the output sentence.
An apparatus for generating a natural language, the apparatus comprising:
- a processor configured to:
- convert an input sentence to a first vector using a first neural network model-based encoder;
- determine whether a control word is to be provided based on a criterion;
- and
- convert the first vector to an output sentence using a neural network model-based decoder based on whether the control word is to be provided.
An apparatus for training a natural language generation apparatus, the apparatus comprising:
- a processor configured to:
- convert a training sentence to a first vector using a first neural network model-based encoder;
- determine whether a control word is to be provided based on a criterion;
- convert the first vector to an output sentence using a neural network model-based decoder based on whether the control word is to be provided;
- and
- train the first encoder and the decoder by evaluating an accuracy of the output sentence.
Dependencies are inferred from the claim text.