US 11,908,460 B2 · Using a generative adversarial network to train a semantic parser of a dialog system

Abstract

Disclosed herein are techniques for using a generative adversarial network (GAN) to train a semantic parser of a dialog system. A method described herein involves accessing seed data that includes seed tuples. Each seed tuple includes a respective seed utterance and a respective seed logical form corresponding to the respective seed utterance. The method further includes training a semantic parser and a discriminator in a GAN. The semantic parser learns to map utterances to logical forms based on output from the discriminator, and the discriminator learns to recognize authentic logical forms based on output from the semantic parser. The semantic parser may then be integrated into a dialog system.

Claims20

Dependent claims

A method for training a semantic parser of a dialog system, the method comprising:

accessing seed data comprising seed tuples, each seed tuple of the seed data comprising a respective seed utterance and a respective seed logical form corresponding to the respective seed utterance;
and
training a semantic parser and a discriminator in a generative adversarial network (GAN), wherein, in the GAN, the semantic parser learns to map utterances to logical forms and the discriminator learns to recognize authentic logical forms, wherein training the semantic parser and the discriminator in the GAN comprises:
training the discriminator with a first portion of the seed data based on output from the semantic parser to determine an updated discriminator;
and
training the semantic parser with a second portion of the seed data based on output from the updated discriminator to determine an updated semantic parser;
wherein the semantic parser is useable as part of a dialog system.

4 more elements

A computer-program product for training a semantic parser of a dialog system, the computer-program product comprising a computer-readable storage medium having program instructions embodied thereon, the program instructions executable by one or more processors to cause the one or more processors to perform a method comprising:

accessing seed data comprising seed tuples, each seed tuple of the seed data comprising a respective seed utterance and a respective seed logical form corresponding to the respective seed utterance;
training a discriminator of a generative adversarial network (GAN) based on a semantic parser of the GAN, wherein training the discriminator comprises:
inputting random utterances to the semantic parser;
determining, by the semantic parser, generated logical forms based on the random utterances;
inputting, to the discriminator, tuples comprising generated tuples and a subset of seed tuples selected from the seed data, wherein the generated tuples comprise the random utterances and the generated logical forms;
determining, by the discriminator, predicted authenticities of the tuples;
comparing the predicted authenticities to actual authenticities of the tuples;
and
updating the discriminator based on comparing the predicted authenticities to the actual authenticities of the tuples;
and
training the semantic parser of the GAN based on the discriminator of the GAN, wherein training the semantic parser comprises:
inputting seed utterances to the semantic parser, wherein the seed utterances are selected from the seed tuples;
determining, by the semantic parser, additional generated logical forms based on the seed utterances;
inputting, to the discriminator, additional generated tuples comprising the seed utterances from the seed data and the additional generated logical forms;
determining, by the discriminator, additional predicted authenticities of the additional generated tuples;
comparing the additional predicted authenticities to selected authenticities of the additional generated tuples;
and
updating the semantic parser based on comparing the additional predicted authenticities to the selected authenticities of the additional generated tuples,
wherein the semantic parser is useable to map utterances to logical forms in a dialog system.

16 more elements

Dependencies are inferred from the claim text.

Using a generative adversarial network to train a semantic parser of a dialog system

Life of this patent

Using a generative adversarial network to train a semantic parser of a dialog system

Life of this patent

Abstract

Claims20