Abstract: In certain implementations, intent prediction is provided for a natural language utterance based on a portion of the natural language utterance prior to a system detection of an end of the natural language utterance. In some implementations, a first portion of a natural language utterance of a user may be received. Speech recognition may be performed on the first portion of the natural language utterance to recognize one or more words of the first portion of the natural language utterance. Context information for the natural language utterance may be obtained. Prior to a detection of an end of the natural language utterance, a first intent may be predicted based on the one or more words of the first portion and the context information. One or more user requests may be determined based on the first predicted intent.

Abstract: The disclosure relates to systems and methods for generating semantic parsers based on automatically generated operators and user-designated utterances relating to the operators for use in natural language processing. The system may automatically generate multiple operators that each express a respective computer-executable instruction that resolves a request. These operators may be expressed in a manner that is machine-readable and not necessarily for consumption by a human user. The system may generate a canonical statement that expresses the request in a first manner that a human user would be able to understand. The system may generate a task, such as crowd-sourced task, for a human user to provide an utterance that conveys the canonical statement in a second manner different than the first manner. By doing so, the system may rapidly build operators and learn how humans would utter requests resolved by instructions encoded in the operators for building semantic parsers.

Abstract: Systems and methods of validating transcriptions of natural language content using crowdsourced validation jobs are provided herein. In various implementations, a transcription pair comprising natural language content and text corresponding to a transcription of the natural language content may be gathered. A group of validation devices may be selected for reviewing the transcription pair. A crowdsourced validation job may be created for the group of validation devices. The crowdsourced validation job may be provided to the group of validation devices. One or more votes representing whether or not the text accurately represents the natural language content may be received from the group of validation devices. Based on the one or more votes received, the transcription pair may be stored in a validated transcription library, which may be used to process end-user voice data.

Abstract: The disclosure relates to transferred learning from a first language (e.g., a source language for which a semantic parser has been defined) to a second language (e.g., a target language for which a semantic parser has not been defined). A system may use knowledge from a trained model in one language to model another language. For example, the system may transfer knowledge of a semantic parser from a first (e.g., source) language to a second (e.g., target) language. Such transfer of knowledge may occur and be useful when the first language has sufficient training data but the second language has insufficient training data. The foregoing transfer of knowledge may extend the semantic parser for multiple languages (e.g., the first language and the second language).

Abstract: The disclosure relates to methods, systems and other embodiments directed to determining an information domain match for a natural language (NL) input (e.g., a spoken utterance), and confirming whether the NL input is correctly matched to the information domain. For example, after receiving an NL input, a first information domain to which the NL input belongs and a feature value set may be determined based on a semantic pattern matching technique. Further, a second information domain to which the NL input belongs, and a corresponding confidence score related to the second information domain may be determined. The second information domain may be determined based on a first statistical classification technique. Based on the determined feature value set and the confidence score related to the second information domain, it may be confirmed whether the NL input correctly belongs to the first information domain, e.g., based on a second statistical classification technique.

Abstract: A system and method is provided for adjusting natural language conversations between a human user and a computer based on the human user's cognitive state and/or situational state, particularly when the user is operating a vehicle. The system may disengage in conversation with the user (e.g., the driver) or take other actions based on various situational and/or user states. For example, the system may disengage conversation when the system detects that the driving situation is complex (e.g., car merging onto a highway, turning right with multiple pedestrians trying to cross, etc.). The system may (in addition or instead) sense the user's cognitive load and disengage conversation based on the cognitive load. The system may alter its personality (e.g. by engaging in mentally non-taxing conversations such as telling jokes based on situational and/or user states.

Abstract: A system and method of tagging utterances with Named Entity Recognition (“NER”) labels using unmanaged crowds is provided. The system may generate various annotation jobs in which a user, among a crowd, is asked to tag which parts of an utterance, if any, relate to various entities associated with a domain. For a given domain that is associated with a number of entities that exceeds a threshold N value, multiple batches of jobs (each batch having jobs that have a limited number of entities for tagging) may be used to tag a given utterance from that domain. This reduces the cognitive load imposed on a user, and prevents the user from having to tag more than N entities. As such, a domain with a large number of entities may be tagged efficiently by crowd participants without overloading each crowd participant with too many entities to tag.

Abstract: A conversational, natural language voice user interface may provide an integrated voice navigation services environment. The voice user interface may enable a user to make natural language requests relating to various navigation services, and further, may interact with the user in a cooperative, conversational dialogue to resolve the requests. Through dynamic awareness of context, available sources of information, domain knowledge, user behavior and preferences, and external systems and devices, among other things, the voice user interface may provide an integrated environment in which the user can speak conversationally, using natural language, to issue queries, commands, or other requests relating to the navigation services provided in the environment.

Abstract: A system and method is provided of disambiguating natural language processing requests based on smart matching, request confirmations that are used until ambiguities are resolved, and machine learning. Smart matching may match entities (e.g., contact names, place names, etc.) based on user information such as call logs, user preferences, etc. If multiple matches are found and disambiguation has not yet been learned by the system, the system may request that the user identify the intended entity. On the other hand, if disambiguation has been learned by the system, the system may execute the request without confirmations. The system may use a record of confirmations and/or other information to continuously learn a user's inputs in order to reduce ambiguities and no longer prompt for confirmations.

Abstract: A system and method of tagging utterances with Named Entity Recognition (“NER”) labels using unmanaged crowds is provided. The system may generate various annotation jobs in which a user, among a crowd, is asked to tag which parts of an utterance, if any, relate to various entities associated with a domain. For a given domain that is associated with a number of entities that exceeds a threshold N value, multiple batches of jobs (each batch having jobs that have a limited number of entities for tagging) may be used to tag a given utterance from that domain. This reduces the cognitive load imposed on a user, and prevents the user from having to tag more than N entities. As such, a domain with a large number of entities may be tagged efficiently by crowd participants without overloading each crowd participant with too many entities to tag.

Abstract: A system and method for processing multi-modal device interactions in a natural language voice services environment may be provided. In particular, one or more multi-modal device interactions may be received in a natural language voice services environment that includes one or more electronic devices. The multi-modal device interactions may include a non-voice interaction with at least one of the electronic devices or an application associated therewith, and may further include a natural language utterance relating to the non-voice interaction. Context relating to the non-voice interaction and the natural language utterance may be extracted and combined to determine an intent of the multi-modal device interaction, and a request may then be routed to one or more of the electronic devices based on the determined intent of the multi-modal device interaction.

Abstract: Systems and methods of validating transcriptions of natural language content using crowdsourced validation jobs are provided herein. In various implementations, a transcription pair comprising natural language content and text corresponding to a transcription of the natural language content may be gathered. A group of validation devices may be selected for reviewing the transcription pair. A crowdsourced validation job may be created for the group of validation devices. The crowdsourced validation job may be provided to the group of validation devices. One or more votes representing whether or not the text accurately represents the natural language content may be received from the group of validation devices. Based on the one or more votes received, the transcription pair may be stored in a validated transcription library, which may be used to process end-user voice data.

Abstract: The invention relates to a system and method for integrating domain information into state transitions of a Finite State Transducer (“FST”) for natural language processing. A system may integrate semantic parsing and information retrieval from an information domain to generate an FST parser that represents the information domain. The FST parser may include a plurality of FST paths, at least one of which may be used to generate a meaning representation from a natural language input. As such, the system may perform domain-based semantic parsing of a natural language input, generating more robust meaning representations using domain information. The system may be applied to a wide range of natural language applications that use natural language input from a user such as, for example, natural language interfaces to computing systems, communication with robots in natural language, personalized digital assistants, question-answer query systems, and/or other natural language processing applications.

Abstract: Systems and methods gathering text commands in response to a command context using a first crowdsourced are discussed herein. A command context for a natural language processing system may be identified, where the command context is associated with a command context condition to provide commands to the natural language processing system. One or more command creators associated with one or more command creation devices may be selected. A first application one the one or more command creation devices may be configured to display command creation instructions for each of the one or more command creators to provide text commands that satisfy the command context, and to display a field for capturing a user-generated text entry to satisfy the command creation condition in accordance with the command creation instructions. Systems and methods for reviewing the text commands using second and crowdsourced jobs are also presented herein.

Abstract: In certain implementations, follow-up responses may be provided for prior natural language inputs of a user. As an example, a natural language input associated with a user may be received at a computer system. A determination of whether information sufficient for providing an adequate response to the natural language input is currently accessible to the computer system may be effectuated. A first response to the natural language input (that indicates that a follow-up response will be provided) may be provided based on a determination that information sufficient for providing an adequate response to the natural language input is not currently accessible. Information sufficient for providing an adequate response to the natural language input may be received. A second response to the natural language input may then be provided based on the received sufficient information.

Abstract: A system and method for an integrated, multi-modal, multi-device natural language voice services environment may be provided. In particular, the environment may include a plurality of voice-enabled devices each having intent determination capabilities for processing multi-modal natural language inputs in addition to knowledge of the intent determination capabilities of other devices in the environment. Further, the environment may be arranged in a centralized manner, a distributed peer-to-peer manner, or various combinations thereof. As such, the various devices may cooperate to determine intent of multi-modal natural language inputs, and commands, queries, or other requests may be routed to one or more of the devices best suited to take action in response thereto.

Abstract: Systems and methods gathering text commands in response to a command context using a first crowdsourced are discussed herein. A command context for a natural language processing system may be identified, where the command context is associated with a command context condition to provide commands to the natural language processing system. One or more command creators associated with one or more command creation devices may be selected. A first application one the one or more command creation devices may be configured to display command creation instructions for each of the one or more command creators to provide text commands that satisfy the command context, and to display a field for capturing a user-generated text entry to satisfy the command creation condition in accordance with the command creation instructions. Systems and methods for reviewing the text commands using second and crowdsourced jobs are also presented herein.

Abstract: A system and method of recording utterances for building Named Entity Recognition (“NER”) models, which are used to build dialog systems in which a computer listens and responds to human voice dialog. Utterances to be uttered may be provided to users through their mobile devices, which may record the user uttering (e.g., verbalizing, speaking, etc.) the utterances and upload the recording to a computer for processing. The use of the user's mobile device, which is programmed with an utterance collection application (e.g., configured as a mobile app), facilitates the use of crowd-sourcing human intelligence tasking for widespread collection of utterances from a population of users. As such, obtaining large datasets for building NER models may be facilitated by the system and method disclosed herein.

Abstract: A conversational, natural language voice user interface may provide an integrated voice navigation services environment. The voice user interface may enable a user to make natural language requests relating to various navigation services, and further, may interact with the user in a cooperative, conversational dialogue to resolve the requests. Through dynamic awareness of context, available sources of information, domain knowledge, user behavior and preferences, and external systems and devices, among other things, the voice user interface may provide an integrated environment in which the user can speak conversationally, using natural language, to issue queries, commands, or other requests relating to the navigation services provided in the environment.

Abstract: In certain implementations, follow-up responses may be provided for prior natural language inputs of a user. As an example, a natural language input associated with a user may be received at a computer system. A determination of whether information sufficient for providing an adequate response to the natural language input is currently accessible to the computer system may be effectuated. A first response to the natural language input (that indicates that a follow-up response will be provided) may be provided based on a determination that information sufficient for providing an adequate response to the natural language input is not currently accessible. Information sufficient for providing an adequate response to the natural language input may be received. A second response to the natural language input may then be provided based on the received sufficient information.