Abstract: In a method and device for cell (re)selection in an EMB-based non-terrestrial network. the method of a UE includes the steps of: configuring a candidate cell set which is a set of one or more neighboring cells having a cell timer value greater than the sum of a cell service timer value of a serving cell and a second value among at least one neighboring cell; selecting a candidate cell among the one or more neighboring cells on the basis of the SAT service timer value of each of the one or more neighboring cells belonging to the candidate cell set; and performing a procedure for configuring a connection with the candidate cell.

Abstract: A method and a device for handover in an earth-moving beam (EMB)-based non-terrestrial network are disclosed. A method of a user equipment (UE) comprises the steps of: receiving, from a first cell of a satellite, first handover configuration information including information about a first cell service timer indicating the time at which a communication service can be provided in the first cell; reducing the first cell service timer according to a first reduction interval; and triggering an intra-satellite (intra-SAT) handover procedure if the first cell service timer has a first critical value or less.

Abstract: Provided is a method of constructing a neural network translation model. The method includes generating a first neural network translation model learning a feature of source domain data used in an unspecific field, generating a second neural network translation model learning a feature of target domain data used in a specific field, generating a third neural network translation model learning a common feature of the source domain data and the target domain data; and generating a combiner combining translation results of the first to third neural network translation models.

Abstract: Provided are a method and apparatus for constructing a compact translation model that may be installed on a terminal on the basis of a pre-built reference model, in which a pre-built reference model is miniaturized through a parameter imitation learning and is efficiently compressed through a tree search structure imitation learning without degrading the translation performance. The compact translation model provides translation accuracy and speed in a terminal environment that is limited in network, memory, and computation performance.

Abstract: A real-time utterance verification system according to the present invention includes a speech recognition unit configured to recognize an utterance of an utterer, a memory configured to store a program for verifying the utterance of the utterer in real time, and a processor configured to execute the program stored in the memory, wherein, upon executing the program, the processor generates and stores a list of the utterance of the utterer, performs a semantic analysis on each utterance included in the list, and generates, when the utterance is determined to be an inappropriate utterance for a listener as a result of the semantic analysis, utterance restricting information corresponding to the inappropriate utterance.

Abstract: Provided are a system and method for end-to-end neural machine translation. The method of end-to-end neural machine translation includes performing learning including a READ token on an end-to-end neural machine translation network, performing learning on an action network to learn a position of an actual segmentation point, and performing entire network re-learning on the end-to-end neural machine translation network and the action network.

Abstract: Provided are an apparatus and method for automatic translation, and more specifically, an apparatus and method for automatic translation between low-resource languages lacking learning data. The apparatus includes an inputter configured to receive a source language, which is a low-resource language, and a third language abundant in resources compared to the low-resource language, a memory configured to store a program for performing automatic translation between the source language, which is the low-resource language, and a target language using the third language, and a processor configured to execute the program, wherein the processor performs the automatic translation using a third language vocabulary embedding vector.

Abstract: Provided are a method and apparatus for constructing a compact translation model that may be installed on a terminal on the basis of a pre-built reference model, in which a pre-built reference model is miniaturized through a parameter imitation learning and is efficiently compressed through a tree search structure imitation learning without degrading the translation performance. The compact translation model provides translation accuracy and speed in a terminal environment that is limited in network, memory, and computation performance.

Abstract: A method and a device for training a neural machine translation model to ensure high translation performance even in a language pair or a domain having a small amount of parallel corpora and solving the problems of over-translation and under-translation caused by the inaccuracy of word-alignment information of an attention network. To this end, bidirectional neural machine translation models are built, and single language corpora are made available for training on the basis of symmetric relation between the models. Also, incomplete alignment information between attention networks of the bidirectional neural machine translation models is normalized to have orthogonal relation so that accurate alignment information may be learned.

Abstract: The present invention relates to a device of simultaneous interpretation based on real-time extraction of an interpretation unit, the device including a voice recognition module configured to recognize voice units as sentence units or translation units from vocalized speech that is input in real time, a real-time interpretation unit extraction module configured to form one or more of the voice units into an interpretation unit, and a real-time interpretation module configured to perform an interpretation task for each interpretation unit formed by the real-time interpretation unit extraction module.

Abstract: An incremental self-learning based dialogue apparatus for dialogue knowledge includes a dialogue processing unit configured to determine a intention of a user utterance by using a knowledge base and perform processing or a response suitable for the user intention, a dialogue establishment unit configured to automatically learn a user intention stored in a intention annotated learning corpus, store information about the learned user intention in the knowledge base, and edit and manage the knowledge base and the intention annotated learning corpus, and a self-knowledge augmentation unit configured to store a log of a dialogue performed by the dialogue processing unit, detect and classify an error in the stored dialogue log, automatically tag a user intention for the detected and classified error, and store the tagged user intention in the intention annotated learning corpus.

Abstract: A real-time utterance verification system according to the present invention includes a speech recognition unit configured to recognize an utterance of an utterer, a memory configured to store a program for verifying the utterance of the utterer in real time, and a processor configured to execute the program stored in the memory, wherein, upon executing the program, the processor generates and stores a list of the utterance of the utterer, performs a semantic analysis on each utterance included in the list, and generates, when the utterance is determined to be an inappropriate utterance for a listener as a result of the semantic analysis, utterance restricting information corresponding to the inappropriate utterance.

Abstract: Provided is a method of constructing a neural network translation model. The method includes generating a first neural network translation model learning a feature of source domain data used in an unspecific field, generating a second neural network translation model learning a feature of target domain data used in a specific field, generating a third neural network translation model learning a common feature of the source domain data and the target domain data; and generating a combiner combining translation results of the first to third neural network translation models.

Abstract: The present invention relates to a device of simultaneous interpretation based on real-time extraction of an interpretation unit, the device including a voice recognition module configured to recognize voice units as sentence units or translation units from vocalized speech that is input in real time, a real-time interpretation unit extraction module configured to form one or more of the voice units into an interpretation unit, and a real-time interpretation module configured to perform an interpretation task for each interpretation unit formed by the real-time interpretation unit extraction module.

Abstract: An incremental self-learning based dialogue apparatus for dialogue knowledge includes a dialogue processing unit configured to determine a intention of a user utterance by using a knowledge base and perform processing or a response suitable for the user intention, a dialogue establishment unit configured to automatically learn a user intention stored in a intention annotated learning corpus, store information about the learned user intention in the knowledge base, and edit and manage the knowledge base and the intention annotated learning corpus, and a self-knowledge augmentation unit configured to store a log of a dialogue performed by the dialogue processing unit, detect and classify an error in the stored dialogue log, automatically tag a user intention for the detected and classified error, and store the tagged user intention in the intention annotated learning corpus.

Abstract: An apparatus and method for controlling a navigator are disclosed herein. The apparatus includes a natural voice command acquisition unit, an information acquisition unit, a speech language understanding unit, a related information extraction unit, and a dialog management control unit. The natural voice command acquisition unit obtains a natural voice command from a user. The information acquisition unit obtains vehicle data including information about the operation of the navigator and information about the state of the vehicle. The speech language understanding unit converts the natural voice command into a user intention that can be understood by a computer. The related information extraction unit extracts related information that corresponds to the user intention. The dialog management control unit generates a response to the natural voice command based on the related information, the user intention and a dialog history, and controls the navigator in accordance with the conversation response.

Abstract: 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.

Abstract: An apparatus and method for controlling a navigator are disclosed herein. The apparatus includes a natural voice command acquisition unit, an information acquisition unit, a speech language understanding unit, a related information extraction unit, and a dialogue management control unit. The natural voice command acquisition unit obtains a natural voice command from a user. The information acquisition unit obtains vehicle data including information about the operation of the navigator and information about the state of the vehicle. The speech language understanding unit converts the natural voice command into a user intention that can be understood by a computer. The related information extraction unit extracts related information that corresponds to the user intention. The dialogue management control unit generates a response to the natural voice command based on the related information, the user intention and a dialogue history, and controls the navigator in accordance with the conversation response.

Abstract: A wireless measurement device includes a sound acoustic wave (SAW)-based micro sensor converting a wirelessly received pulse signal into an SAW and generating a plurality of pulse signals by reflecting the SAW, and wirelessly transmitting the plurality of pulse signals to measure a variance of an environmental element; and a reader generating and wirelessly transmitting a pulse signal to the SAW-based micro sensor to measure variances in environmental elements.

Abstract: The present invention relates to a translation function and discloses an automatic translation operating device, including: at least one of voice input devices which collects voice signals input by a plurality of speakers and a communication module which receives the voice signals; and a control unit which controls to classify voice signals by speakers from the voice signals and cluster the speaker based voice signals classified in accordance with a predefined condition and then perform voice recognition and translation and a method thereof and a system including the same.