Adversarial learning and generation of dialogue responses
Systems and methods for generating responses to user input such as dialogs, and images are discussed. The system may generate, by a response generation module of at least one server, an optimal generated response to the user communication by applying an generative adversarial network. In some embodiments, the generative adversarial network may include a hierarchical recurrent encoder decoder generative adversarial network including a generator and a discriminator component.
1. An improved method for generating responses to user communications comprising:
receiving, by at least one server communicatively coupled to a user device, a user communication;
applying, by a response generation module of the at least one server, a generative adversarial network to the user communication to generate an optimal generated response to the user communication, the generative adversarial network comprising a generator component and a discriminator component, wherein said discriminator component comprises a convolutional neural network and a recurrent neural network, wherein applying the generative adversarial network comprises:
generating, via the generator component, a plurality of computer generated responses responsive to the user communication, and
selecting, via the discriminator component, an optimal computer generated response from among the generated plurality of computer generated responses; and
transmitting, by the at least one server, the optimal generated response to the user communication to a user of the user device.
2. The method of claim 1 , further comprising training the generative adversarial network by:
providing training data to the generator component of the generative adversarial network,
generating, by the generator component, computer responses to the training data,
evaluating, by the discriminator component of the generative adversarial network, the generated computer responses by at least one comparison to the training data,
ranking, by the discriminator component, the generated computer responses, and
adjusting one or more parameters of the generator component based on the ranking of the generated computer responses.
3. The method of claim 2 , wherein the training data further comprises noise.
4. The method of claim 1 , wherein the generator component further comprises a local attention component.
5. The method of claim 1 , wherein at least one of the generator component and the discriminator component further comprises an encoder and a decoder.
6. An improved method for providing computer generated responses to user communications comprising:
receiving, by a user interface of a user device, a user communication;
transmitting, by the user device, the user communication to at least one server communicatively coupled to the user device;
receiving, by the user device, a computer generated response to the user communication, wherein the computer generated response is generated by applying an generative adversarial network to the user communication, the generative adversarial network comprising a generator component and a discriminator component including a convolutional neural network and a recurrent neural network, wherein applying the generative adversarial network comprises:
generating, via the generator component, a plurality of computer generated responses responsive to the user communication, and
selecting, via the discriminator component, an optimal computer generated response from among the plurality of computer generated responses; and
providing, by the user interface, the computer generated response to a user of the user device.
7. The method of claim 6 , wherein the computer generated response is of the same communication type as the user communication.
8. The method of claim 6 , wherein at least one of the generator component and the discriminator component further comprises an encoder and a decoder.
9. An improved system for generating responses to user communications comprising:
at least one server communicatively coupled to a user device by a network, wherein the at least one server further comprises a non-transitory memory storing computer-readable instructions and at least one processor;
the execution of the computer-readable instructions causing the at least one server to:
receive, from the user device, a user communication;
apply, by a response generation module of the at least one server, an generative adversarial network to the user communication to generate an optimal generated response of the user communication, the generative adversarial network comprising a generator component and a discriminator component, wherein said discriminator component comprises a convolutional neural network and a recurrent neural network, and wherein application of the generative adversarial network further comprises:
generation, via the generator component, of a plurality of computer generated responses responsive to the user communication, and
selection, via the discriminator component, of an optimal computer generated response form among the plurality of computer generated responses; and
transmit the optimal generated response to the user communication to a user of the user device.
10. The system of claim 9 , wherein the optimal generated response is of the same communication type as the user communication.
11. The system of claim 9 , wherein the generator component and the discriminator component are trained by the steps of:
providing training data to the generator component of the generative adversarial network,
generating, by the generator component, computer responses to the training data,
evaluating, by the discriminator component of the generative adversarial network, the generated computer responses by at least one comparison to the training data,
ranking, by the discriminator component, the generated computer responses, and
adjusting one or more parameters of the generator component based on the ranking of the generated computer responses.
12. The system of claim 9 , wherein at least one of the generator component and the discriminator component further comprises an encoder and a decoder.
13. The system of claim 9 , wherein the generator component further comprises a local attention component.