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: Techniques for adapting previously-annotated training examples into updated training examples for training a machine learning model are disclosed. One example includes a computer program that recognizes a find expression, a replacement expression, and a filtering constraint in which the filtering constraint distinguishes a subset of previously-annotated training examples from others of the previously-annotated training examples. An instance of the find expression is identified by the computer program within the subset of the previously-annotated training examples that were identified among the previously-annotated training examples based on the filtering constraint. The instance of the find expression identified within the subset of the previously-annotated training examples is replaced by the computer program with an instance of the replacement expression to obtain an updated subset of training examples.

Abstract: Techniques for adapting previously-annotated training examples into updated training examples for training a machine learning model are disclosed. One example includes a computer program that recognizes a find expression, a replacement expression, and a filtering constraint in which the filtering constraint distinguishes a subset of previously-annotated training examples from others of the previously-annotated training examples. An instance of the find expression is identified by the computer program within the subset of the previously-annotated training examples that were identified among the previously-annotated training examples based on the filtering constraint. The instance of the find expression identified within the subset of the previously-annotated training examples is replaced by the computer program with an instance of the replacement expression to obtain an updated subset of training examples.

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: Techniques for adapting previously-annotated training examples into updated training examples for training a machine learning model are disclosed. One example includes a computer program that recognizes a find expression, a replacement expression, and a filtering constraint in which the filtering constraint distinguishes a subset of previously-annotated training examples from others of the previously-annotated training examples. An instance of the find expression is identified by the computer program within the subset of the previously-annotated training examples that were identified among the previously-annotated training examples based on the filtering constraint. The instance of the find expression identified within the subset of the previously-annotated training examples is replaced by the computer program with an instance of the replacement expression to obtain an updated subset of training examples.

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.

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 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.