Capital One Services, LLC

Diana Mingels

Erik Thomas Mueller

Zachary Kulis

Oluwatobi Olabiyi

Christopher Larson

Tarek Aziz Lahlou

Mathias Menasi

Robert Cowell

Jed Dunn

Amandeep Bakshi

Jeremy Doll

A computing device may represent dialog for output by a chatbot as a response graph. The graph may consist of nodes and edges, both of which may have attributes. The graph may be linked with another graph. There may be one or more traversal paths through the graph. The computing device may represent each traversal path as a row in a database table. Each column in the table row may correspond to: one or more nodes in the traversal path, a condition value, weight, and/or other filtering condition. A computing device may display a graphical user interface that allows a user to add, edit, and/or delete nodes and/or edges of the graph. The user interface may also allow the user to compare two or more graphs. The user interface may generate visualizations of traversal paths of a response graph.

Graph-based natural language generation for conversational systems

Machine classifiers in accordance with embodiments of the invention capture long-term temporal dependencies in particular tasks, such as turn-based dialogues. Machine classifiers may be used to help users to perform tasks indicated by the user. When a user utterance is received, natural language processing techniques may be used to understand the user's intent. Templates may be determined based on the user's intent in the generation of responses to solicit information from the user. A variety of persona attributes may be determined for a user. The persona attributes may be determined based on the user's utterances and/or provided as metadata included with the user's utterances. A response persona may be used to generate responses to the user's utterances such that the generated responses match a tone appropriate to the task. A response persona may be used to generate templates to solicit additional information and/or generate responses appropriate to the task.

Multi-turn dialogue response generation with template generation

Aspects described herein may relate to the determination of data that is indicative of a greater range of speech properties than input text data. The determined data may be used as input to one or more speech processing tasks, such as model training, model validation, model testing, or classification. For example, after a model is trained based on the determined data, the model's performance may exhibit more resilience to a wider range of speech properties. The determined data may include one or more modified versions of the input text data. The one or more modified versions may be associated with the one or more speakers or accents and/or may be associated with one or more levels of semantic similarity in relation to the input text data. The one or more modified versions may be determined based on one or more machine learning algorithms.

Determining input data for speech processing

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Washington, DC, United States of America

Diana Mingels