Abstract: A method for training a dialogue learning model includes presenting, via a user interface of a computing device, an utterance and a list of actions based on the utterance. A selection of an action from the list of actions is received via the user interface. A designated span of the utterance is received via the user interface. The selected action and the designated span of the utterance is provided to a computing system for training the dialogue learning model.

Abstract: An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A method for generating training data for training a natural language processing system comprises loading, into a computer memory, a computer-readable transcript representing an ordered sequence of one or more dialogue events. The method further comprises acquiring a computer-readable command describing an exemplary ordered subsequence of one or more dialogue events from the computer-readable transcript. The method further comprises re-parametrizing the computer-readable command with an alternative semantic parameter. The method further comprises generating an alternative ordered subsequence of one or more dialogue events based on the re-parametrized computer-readable command. The method further comprises outputting, to a data store, an alternative computer-readable transcript including the alternative ordered subsequence of one or more dialogue events, the alternative computer-readable transcript having a predetermined format usable to train the computerized assistant.

Abstract: An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A computing device includes a display configured to present a graphical user interface. The graphical user interface includes a transcript portion configured to display an unannotated transcript representing an ordered sequence of one or more dialogue events involving a client and a computerized assistant, at least one of the dialogue events taking the form of an example client utterance, and an annotation portion configured to display a hierarchical menu including a plurality of candidate utterance annotations. An utterance annotation machine is configured to receive one or more computer inputs selecting, for each of one or more response parameters in the example client utterance, utterance annotations from the hierarchical menu that collectively define a machine-readable interpretation of the example client utterance. An annotated utterance having a predetermined format usable to train the computerized assistant is output to a data store based on the example client utterance.

Abstract: A data collection system is based on a general set of dialogue acts which are derived from a database schema. Crowd workers perform two types of tasks: (i) identification of sensical dialogue paths and (ii) performing context-dependent paraphrasing of these dialogue paths into real dialogues. The end output of the system is a set of training examples of real dialogues which have been annotated with their logical forms. This data can be used to train all three components of the dialogue system: (i) the semantic parser for understanding context-dependent utterances, (ii) the dialogue policy for generating new dialogue acts given the current state, and (iii) the generation system for both deciding what to say and how to render it in natural language.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A computing device includes a display configured to present a graphical user interface. The graphical user interface includes a transcript portion configured to display an unannotated transcript representing an ordered sequence of one or more dialogue events involving a client and a computerized assistant, at least one of the dialogue events taking the form of an example client utterance, and an annotation portion configured to display a hierarchical menu including a plurality of candidate utterance annotations. An utterance annotation machine is configured to receive one or more computer inputs selecting, for each of one or more response parameters in the example client utterance, utterance annotations from the hierarchical menu that collectively define a machine-readable interpretation of the example client utterance.

Abstract: A method for generating training data for training a natural language processing system comprises loading, into a computer memory, a computer-readable transcript representing an ordered sequence of one or more dialogue events. The method further comprises acquiring a computer-readable command describing an exemplary ordered subsequence of one or more dialogue events from the computer-readable transcript. The method further comprises re-parametrizing the computer-readable command with an alternative semantic parameter. The method further comprises generating an alternative ordered subsequence of one or more dialogue events based on the re-parametrized computer-readable command. The method further comprises outputting, to a data store, an alternative computer-readable transcript including the alternative ordered subsequence of one or more dialogue events, the alternative computer-readable transcript having a predetermined format usable to train the computerized assistant.

Abstract: An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

Abstract: A data collection system is based on a general set of dialogue acts which are derived from a database schema. Crowd workers perform two types of tasks: (i) identification of sensical dialogue paths and (ii) performing context-dependent paraphrasing of these dialogue paths into real dialogues. The end output of the system is a set of training examples of real dialogues which have been annotated with their logical forms. This data can be used to train all three components of the dialogue system: (i) the semantic parser for understanding context-dependent utterances, (ii) the dialogue policy for generating new dialogue acts given the current state, and (iii) the generation system for both deciding what to say and how to render it in natural language.