Patent application number | Description | Published |
20130304451 | BUILDING MULTI-LANGUAGE PROCESSES FROM EXISTING SINGLE-LANGUAGE PROCESSES - Processes capable of accepting linguistic input in one or more languages are generated by re-using existing linguistic components associated with a different anchor language, together with machine translation components that translate between the anchor language and the one or more languages. Linguistic input is directed to machine translation components that translate such input from its language into the anchor language. Those existing linguistic components are then utilized to initiate responsive processing and generate output. Optionally, the output is directed through the machine translation components. A language identifier can initially receive linguistic input and identify the language within which such linguistic input is provided to select an appropriate machine translation component. A hybrid process, comprising machine translation components and linguistic components associated with the anchor language, can also serve as an initiating construct from which a single language process is created over time. | 11-14-2013 |
20130346066 | Joint Decoding of Words and Tags for Conversational Understanding - Joint decoding of words and tags may be provided. Upon receiving an input from a user comprising a plurality of elements, the input may be decoded into a word lattice comprising a plurality of words. A tag may be assigned to each of the plurality of words and a most-likely sequence of word-tag pairs may be identified. The most-likely sequence of word-tag pairs may be evaluated to identify an action request from the user. | 12-26-2013 |
20140222422 | SCALING STATISTICAL LANGUAGE UNDERSTANDING SYSTEMS ACROSS DOMAINS AND INTENTS - A scalable statistical language understanding (SLU) system uses a fixed number of understanding models that scale across domains and intents (i.e. single vs. multiple intents per utterance). For each domain added to the SLU system, the fixed number of existing models is updated to reflect the newly added domain. Information that is already included in the existing models and the corresponding training data may be re-used. The fixed models may include a domain detector model, an intent action detector model, an intent object detector model and a slot/entity tagging model. A domain detector identifies different domains identified within an utterance. All/portion of the detected domains are used to determine associated intent actions. For each determined intent action, one or more intent objects are identified. Slot/entity tagging is performed using the determined domains, intent actions, and intent object detector. | 08-07-2014 |
20140278355 | USING HUMAN PERCEPTION IN BUILDING LANGUAGE UNDERSTANDING MODELS - An understanding model is trained to account for human perception of the perceived relative importance of different tagged items (e.g. slot/intent/domain). Instead of treating each tagged item as equally important, human perception is used to adjust the training of the understanding model by associating a perceived weight with each of the different predicted items. The relative perceptual importance of the different items may be modeled using different methods (e.g. as a simple weight vector, a model trained using features (lexical, knowledge, slot type, . . . ), and the like). The perceptual weight vector and/or or model are incorporated into the understanding model training process where items that are perceptually more important are weighted more heavily as compared to the items that are determined by human perception as less important. | 09-18-2014 |
20140379323 | ACTIVE LEARNING USING DIFFERENT KNOWLEDGE SOURCES - Different knowledge sources are automatically accessed to identify and obtain additional data to update a conversational dialog system. One of the knowledge sources is initially selected as a seed source. Seed data from the seed source are used to identify related data in at least one other knowledge source. For example, query click logs may be accessed and searched to determine popular queries that use the seed data. A structured knowledge source may be accessed to determine related nodes to the seed data. A query click log, or some other knowledge source, may be used to determine when a node is related to the seed data. Data that is identified to be related may be used to train a language understanding model or update a schema for the SLU system. The data may be automatically annotated or manually annotated. | 12-25-2014 |
20140379326 | BUILDING CONVERSATIONAL UNDERSTANDING SYSTEMS USING A TOOLSET - Tools are provided to allow developers to enable applications for Conversational Understanding (CU) using assets from a CU service. The tools may be used to select functionality from existing domains, extend the coverage of one or more domains, as well as to create new domains in the CU service. A developer may provide example Natural Language (NL) sentences that are analyzed by the tools to assist the developer in labeling data that is used to update the models in the CU service. For example, the tools may assist a developer in identifying domains, determining intent actions, determining intent objects and determining slots from example NL sentences. After the developer tags all or a portion of the example NL sentences, the models in the CU service are automatically updated and validated. For example, validation tools may be used to determine an accuracy of the model against test data. | 12-25-2014 |
20140379353 | Environmentally aware dialog policies and response generation - Environmental conditions, along with other information, are used to adjust a response of a conversational dialog system. The environmental conditions may be used at different times within the conversational dialog system. For example, the environmental conditions can be used to adjust the dialog manager's output (e.g., the machine action). The dialog state information that is used by the dialog manager includes environmental conditions for the current turn in the dialog as well as environmental conditions for one or more past turns in the dialog. The environmental conditions can also be used after receiving the machine action to adjust the response that is provided to the user. For example, the environmental conditions may affect the machine action that is determined as well as how the action is provided to the user. The dialog manager and the response generation components in the conversational dialog system each use the available environmental conditions. | 12-25-2014 |
20150066496 | ASSIGNMENT OF SEMANTIC LABELS TO A SEQUENCE OF WORDS USING NEURAL NETWORK ARCHITECTURES - Technologies pertaining to slot filling are described herein. A deep neural network, a recurrent neural network, and/or a spatio-temporally deep neural network are configured to assign labels to words in a word sequence set forth in natural language. At least one label is a semantic label that is assigned to at least one word in the word sequence. | 03-05-2015 |