46 results about "Spoken dialog systems" patented technology

The present invention relates to a spoken dialog system and method based on dual dialog management using a hierarchical dialog task library that may increase reutilization of dialog knowledge by constructing and packaging the dialog knowledge based on a task unit having a hierarchical structure, and may construct and process the dialog knowledge using a dialog plan scheme about relationship therebetween by classifying the dialog knowledge based on a task unit to make design of a dialog service convenient, which is different from an existing spoken dialog system in which it is difficult to reuse dialog knowledge since a large amount of construction costs and time is required.

A spoken dialog system stores a history of dialog states in a memory, outputs a system response in a current dialog state, inputs a user utterance, performs speech recognition of the user utterance, to obtain one or a plurality of recognition candidates of the user utterance and likelihoods thereof with respect to the user utterance, calculates a degree of state conformance of each of the current and the preceding dialog states stored in the memory with respect to the user utterance, selects one of the current and the preceding dialog states and one of the recognition candidates based on a combination of the degree of state conformance of each dialog state and the likelihood of each recognition candidate, and performs transition from the current dialog state to a new dialog state based on dialog state selected and recognition candidate selected.

The invention discloses a training method, a training device, a dialogue method and a dialogue system of a dialogue model. The training method comprises the steps of total error constructing, whereina total error function comprising a first error of a natural language understanding model and a second error of a strategy generating model are constructed simultaneously; joint training, wherein withthe target of reducing the total error function, a corpus sample is utilized to jointly train the natural language understanding model and the strategy generating model, input of the natural languageunderstanding model is dialogue sentences, output of the natural language understanding model is internal representation obtained by analyzing the dialogue sentences, input of the strategy generatingmodel at least comprises the output of the natural language understanding model, and output of the strategy generating model is motions aiming at the dialogue sentence. Compared with the prior art, when an error occurs on the natural language understanding model or the strategy generating model, the system can normally conduct dialogues, and the error transferring problem in a traditional methodcaused when the natural language understanding model and a dialogue management model are modeled respectively is solved.

A method and system to parameterize a spoken language dialog system, includes providing a storage area to store at least one parameter value, integrating a subcomponent with at least one module of the spoken language dialog system, and configuring the subcomponent to access the at least one parameter value and to adjust an operation or output of the module based on the at least one parameter value.

A method of generating a natural language model for use in a spoken dialog system is disclosed. The method comprises using sample utterances and creating a number of hand crafted rules for each call-type defined in a labeling guide. A first NLU model is generated and tested using the hand crafted rules and sample utterances. A second NLU model is built using the sample utterances as new training data and using the hand crafted rules. The second NLU model is tested for performance using a first batch of labeled data. A series of NLU models are built by adding a previous batch of labeled data to training data and using a new batch of labeling data as test data to generate the series of NLU models with training data that increases constantly. If not all the labeling data is received, the method comprises repeating the step of building a series of NLU models until all labeling data is received. After all the training data is received, at least once, the method comprises building a third NLU model using all the labeling data, wherein the third NLU model is used in generating the spoken dialog service.

A spoken dialog system comprises a speech recognition unit for recognizing a user's input speech to generate a character sequence corresponding thereto; a sentence contents database for storing therein a plurality of sentence contents; a knowledge search unit for searching through the sentence contents to find a match for the character sequence, in the sentence contents database; a dialog model unit for delivering the character sequence to the knowledge search unit to receive the sentence contents therefrom, and setting a dialog model by using the sentence contents; a system response unit for generating an output sentence which harmonizes with the user's input speech or expresses a situation of the system; and a speech synthesis unit for converting the output sentence into the output speech.

The invention discloses a method and a system for information processing for a question and answer robot. The method comprises the following steps of obtaining multi-mode interaction information; calling user entity information from a memory system, wherein the user entity information is used for representing the habits, attributes, identity and behaviors of a user; according to the multi-mode interaction information and the user entity information, determining a result corresponding to a current question and answer process. The method has the advantages that on the basis of the multi-mode interaction, the user entity information is deeply mined, so that a user information support is provided for the whole multi-mode interaction process; compared with a traditional dialogue system which can only complete simple dialogues with the user, intents of the user can be more accurately guessed, an intimate answer is provided according to the actual conditions of the user, and the user experience is greatly improved.

A spoken dialog system and method having a dialog management module are disclosed. The dialog management module includes a plurality of dialog motivators for handling various operations during a spoken dialog. The dialog motivators comprise an error-handling, disambiguation, assumption, confirmation, missing information, and continuation. The spoken dialog system uses the assumption dialog motivator in either a-priori or a-posteriori modes. A-priori assumption is based on predefined requirements for the call flow and a-posteriori assumption can work with the confirmation dialog motivator to assume the content of received user input and confirm received user input.

In order to solve a conventional problem that, after a series of dialog between a user and a spoken dialog device has progressed to some extent, that user or another user cannot see or recognize a previous dialog status, a cross-lingual spoken dialog system is provided wherein, in a case in which an instruction from a user terminal is received by a pairing server, dialog information stored in a storage medium is transmitted to the user terminal. Accordingly, even after a series of dialog between a user and the spoken dialog device has progressed to some extent, that user or another user can see or recognize a previous dialog status.

Example embodiments of the present invention may include a method that includes collecting caller response timings to each of a plurality of dialog states conducted during a call, and estimating a plurality of parameters based on the caller response timings. The method may also include selecting a response completeness value responsive to the estimated plurality of parameters, the response completeness value is used to calculate at least one optimal timeout value. The method may also include selecting the at least one optimal timeout value, and setting the at least one optimal timeout value for each of the corresponding dialog states. The timeout value(s) may be used for subsequent calls to provide optimal user satisfaction and call success rates.

A spoken language understanding and rewriting method based on a commercial dialogue system comprises the following steps: identifying and acquiring domain information related to an utterance accordingto the utterance input by a user; according to the field related to the utterance, identifying and acquiring intention information contained in the utterance; detecting slot position information contained in the user utterance according to the intention information, and storing the detected slot position information; rewritting the utterance input by the user according to the domain information,the intention information and the stored slot position information, and complementing the utterance information; selecting the sub-dialogue system to provide service according to the domain information and intention information of the utterance input by the user. According to the method, the problem of semantic transmission blockage easily appearing in a commercial dialogue system can be solved. Through the application of the method, when the sub-dialogue system is switched due to the change of the user demand, the sub-dialogue system can know the user demand according to the rewritten user utterance and is not perceived by the user, so that the use experience of the user can be ensured to the maximum extent.

The invention discloses a game customer-service dialoguing system based on a deep neural network. The game customer-service dialoguing system based on the deep neural network comprises a dialoguing control module, a user querying module, a problem model module, an answer model module and a knowledge base querying module, wherein the dialoguing control module receives query languages from users andjudges to conduct knowledge base querying or switch a manual service; the problem model module encodes problems queried by the users through a Seq2Seq mechanism of the deep neural network, obtains formalized languages capable of being used for querying the knowledge base, and inputs the formalized languages to the knowledge base querying module; the knowledge base querying module receives the input result from the problem model module, queries a knowledge base, and outputs the query result to the answer model module; the answer model module encodes the query result of the knowledge base through the Seq2Seq mechanism of the deep neural network, and outputs natural languages capable of being understood by the users to a user interactive interface. By means of the game customer-service dialoguing system based on the deep neural network, game users can rapidly obtain feedback in the game playing process.

A computer-implemented method, computer program product, and system are provided for providing a recommendation. The method includes receiving, by a conversation system, a query from a user. The method also includes inputting the query into an intent-entity model to obtain a first output. The method further includes inputting the query into a question-answering system to obtain a second output. The method additionally includes analyzing the first output from the intent-entity model to calculate a value of degree of ambiguity of the first output to the query. The method also includes analyzing the query and the second output from the question-answering system to calculate a value of extent of satisfaction to which the second output satisfies the query. The method further includes determining one or more recommendations using the degree of ambiguity and the extent of satisfaction. The method additionally includes outputting the one or more recommendations to the user.

The invention provides a method and a device for ordering alternative answers of an artificial intelligence conversational system. The method comprises the following steps: acquiring an alternative answer detailed list according to information input by a user, then acquiring knowledge information related to input information according to a knowledge graph, and finally ordering the alternative answers according to the related knowledge information, wherein the knowledge information comprises but is not limited to common-sense knowledge and/or specialized knowledge. In the process of ordering the alternative answers, on the basis of taking relevancy between user input and answers into account, the influences of common-sense knowledge and/or specialized knowledge are further taken into account, the reply of the system can accord with common sense and logic preferably, and thus the user requirement is met preferably.