Natural language generation using pinned text and multiple discriminators
AssigneeJungle Disk, L.L.C.
InventorMichael DeFelice
Life of this patent
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Abstract
A personality model is created for a population and used as an input to a text generation system. Alternative texts are created based upon the emotional effect of the generated text. Certain words or phrases are “pinned” in the output, reducing the variability of the generated text so as to preserve required information content, and a number of tests provide input to a discriminator network so that proposed outputs both match an outside objective regarding the information content, emotional affect, and grammatical acceptability. A feedback loop provides new “ground truth” data points for refining the personality model and associated generated text.
Claims21
A system for generating natural language, the system comprising:
- a prospect modeling component, operable to correlate known information about a target prospect personality with a quantitative personality model, the quantitative personality model being expressed as a vector indicating the relative expression of a plurality of relatively mutually orthogonal personality traits;
- and
- a neural sequence-to-sequence encoder-decoder,
- wherein the encoder is operable to deconstruct a source text and represent it as a sequence of weights on a pre-built conditional text model,
- wherein the decoder is operable to create a generated text with approximately equal semantic content but differing syntax and word choice, and
- wherein the syntax and word choice of the generated text varies as a function of the expression of the quantitative personality model.
A method for generating natural language, comprising:
- providing a quantitative personality model, using a prospect modeling component, based on correlating known information about a target prospect personality, the quantitative personality model being expressed as a vector indicating a relative expression of a plurality of relatively mutually orthogonal personality traits;
- deconstructing a source text and representing it as a sequence of weights on a pre-built conditional text model, using an encoder of a neural sequence-to-sequence encoder-decoder;
- and
- creating a generated text with approximately equal semantic content but differing syntax and word choice, using a decoder of the neural sequence-to-sequence encoder-decoder,
- wherein the syntax word choice of the generated text varies as a function of the expression of the quantitative personality model.
A non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to:
- provide a quantitative personality model, using a prospect modeling component, based on correlating known information about a target prospect personality, the quantitative personality model being expressed as a vector indicating a relative expression of a plurality of relatively mutually orthogonal personality traits;
- deconstruct a source text and represent it as a sequence of weights on a pre-built conditional text model, using an encoder of a neural sequence-to-sequence encoder-decoder;
- and
- create a generated text with approximately equal semantic content but differing syntax and word choice, using a decoder of the neural sequence-to-sequence encoder-decoder,
- wherein the syntax word choice of the generated text varies as a function of the expression of the quantitative personality model.
Dependencies are inferred from the claim text.