Capital One Services, LLC

Anish Khazane

Alan Salimov

Omar Florez Choque

Erik Thomas Mueller

Oluwatobi Olabiyi

Various embodiments may be generally directed to the use of an adversarial learning framework for persona-based dialogue modeling. In some embodiments, automated multi-turn dialogue response generation may be performed using a persona-based hierarchical recurrent encoder-decoder-based generative adversarial network (phredGAN). Such a phredGAN may feature a persona-based hierarchical recurrent encoder-decoder (PHRED) generator and a conditional discriminator. In some embodiments, the conditional discriminator may include an adversarial discriminator that is provided with attribute representations as inputs. In some other embodiments, the conditional discriminator may include an attribute discriminator, and attribute representations may be handled as targets of the attribute discriminator. The embodiments are not limited in this context.

Adversarial learning framework for persona-based dialogue modeling

Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

Techniques to add smart device information to machine learning for increased context

Memory augmented neural networks may use one or more neural encoders to transform input data into distributed representations and a memory module to store the representations with individual addresses. Memory augmented neural networks allow for few-shot learning capabilities because latent representations are persistent between training examples and gradient-based updates affect only certain memory locations via content-based lookups. When a query vector is not found in memory and the memory is full, existing memories that are positively associated with a particular representation may be identified, redundant memories may be aged, and updated memories may be generated. These updated memories retain relevant information acquired during training and reduce redundancy in the memories stored using the memory module, thereby improving the efficiency of data storage and reducing overfitting of data typically encountered with existing neural networks using memory modules.

Response generation using memory augmented deep neural networks

Systems, methods, and articles of manufacture for learning design policies based on user interactions. One example includes determining a first task for an environment, receiving data from a plurality of data sources, determining a first time step associated with the received data, determining a plurality of candidate actions for the determined first time step, computing a respective probability value of each candidate action achieving the first task at the first time step based on a first machine learning (ML) model, determining that a first candidate action has a greater probability value for achieving the first task at the first time step relative to the remaining plurality of candidate actions, determining that the first candidate action has not been implemented in the environment at the first time step, and generating an indication specifying to implement the first candidate action as part of a policy to achieve the first task.

Learning: learning design policies based on interactions

Adversarial, learning framework for persona-based dialogue modeling

Memory management in memory augmented deep neural networks

Techniques for bi-modal learning in a financial context are described. These techniques are configured to improve a user's financial acumen and bring the user into an optimal financial state. Some of these techniques are embodied in a device that operates financial education lessons specifically configured for the improving the user's current financial state. These techniques may implement rewards/penalties (in tokens) for correct/incorrect user responses to financial decisions being presented in these lessons for user to make. By exploiting the user's desire for rewards and tokens and the desire to improve the user's current financial state, these techniques may leverage machine learning techniques to identify an appropriate financial education lesson that is most likely to have a positive effect on the user. Over time, administrating the financial education lessons builds customer loyalty to the device that implements these techniques. Other embodiments are described and claimed.

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San Francisco, CA, United States of America

Anish Khazane