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 generates natural language content. The system generates and maintains a dialogue state representation having a process view, query view, and data view. The three-view dialogue state representation is continuously updated during discourse between an agent and a user, and rules can be automatically generated based on the discourse. Upon a content generation event, an object description can be generated based on the dialogue state representation. A string is then determined from the object description, using a hybrid approach of the automatically generated rules and other rules learned from annotation and other user input. The string is translated to text or speech and output by the agent. The present system also incorporates learning techniques, for example when ranking output and processing annotation templates.

Abstract: A conversational system receives an utterance, and a parser performs a parsing operation on the utterance, resulting in some words being parsed and some words not being parsed. For the words that are not parsed, words or phrases determined to be unimportant are ignored. The resulting unparsed words are processed to determine the likelihood they are important and whether they should be addressed by the automated assistant. For example, if a score associated with an important unparsed word achieves a particular threshold, then a course of action to take for the utterance may include providing a message that the portion of the utterance associated with the important unparsed word cannot be handled.

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 eliminates alignment processing and performs TTS functionality using a new neural architecture. The neural architecture includes an encoder and a decoder. The encoder receives an input and encodes it into vectors. The encoder applies a sequence of transformations to the input and generates a vector representing the entire sentence. The decoder takes the encoding and outputs an audio file, which can include compressed audio frames.

Abstract: An automated assistant automatically recognizes speech, decode paraphrases in the recognized speech, performs an action or task based on the decoder output, and provides a response to the user. The response may be text or audio, and may be translated to include paraphrasing. The automatically recognized speech may be processed to determine partitions in the speech, which may be in turn processed to identify paraphrases in the partitions. A decoder may process an input utterance text to identify paraphrases content to include in a segment or sentence. The decoder may paraphrase the input utterance to make the utterance, updated with one or more paraphrases, more easily parsed by a parser. A translator may process a generated response to make the response sound more natural. The translator may replace content of the generated response with paraphrase content based on the state of the conversation with the user, including salience data.

Abstract: The intonation model of the present technology disclosed herein assigns different words within a sentence to be prominent, analyzes multiple prominence possibilities (in some cases, all prominence possibilities), and learns parameters of the model using large amounts of data. Unlike previous systems, intonation patterns are discovered from data.