Botbotbotbot Inc.

Antoine Raux

Yi Ma

Paul Yang

Felicia Wong

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating responses using task-independent conversational systems are provided. In one example method, a response to a user text input is generated by updating a state of the conversation based on the user text input, generating a conversational (task-independent) output, and determining whether to provide a conversational response based on the conversational output, or to additionally generate a task-specific output.

Systems and methods for generating a response based on task-independent conversational responses or task-specific responses

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for secure data transmission using natural language. One of the methods includes: obtaining sensitive information for a user; obtaining a natural language key for the user, wherein the natural language key for the user includes one or more natural language tokens; generating decoding data for the sensitive information for the user, wherein generating the decoding data comprises: for each place in the sensitive information for the user: assigning a respective one of the natural language tokens in the natural language key for the user to the value at the place, and generating one or more respective dummy natural language tokens for each value of the respective set of possible values for the place other than the value at the place; and providing the decoding data for use in decoding the natural language key into the sensitive information.

Secure data transmission using natural language

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating responses using task-independent conversational systems.

Task-independent conversational systems

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating additional training data for a natural language understanding engine. One of the methods includes: obtaining data identifying (i) a first input conversational turn and (ii) a first annotation, determining that the first annotation accurately characterized the first input conversational turn, determining that the natural language understanding engine is likely to generate inaccurate annotations of other conversational turns that are similar to the first input conversational turn, in response to the determining, obtaining one or more first paraphrases of the first input conversational turn; and generating, for each of the one or more first paraphrases, a respective first training example that identifies the first annotation as the correct annotation for the first paraphrase; and training the natural language understanding engine on at least the first training examples.

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Menlo Park, CA, United States of America

Antoine Raux