Samsung Electronics Co., Ltd.

Hwidong Na

Hoshik Lee

Jihyun Lee

Min-Joong Lee

Hyohyeong Kang

Hogyeong Kim

Hodong Lee

Inchul Song

YoungMin Kim

Young-Seok Kim

Young Sang Choi

Minyoung Mun

Seongmin Ok

Youngki Park

Hos Hik Lee

Hyoh Yeong Kang

Hog Yeong Kim

A real-time processor-implemented translation method and apparatus is provided. The real-time translation method includes receiving a content, determining a delay time for real-time translation based on a silence interval of the received content and an utterance interval of the received content, generating a translation result by translating a language used in the received content, and synthesizing the translation result and the received content.

Method and apparatus with real-time translation

A translation method includes: selecting a source word from a source sentence; generating mapping information including location information of the selected source word mapped to the selected source word in the source sentence; and correcting a target word, which is generated by translating the source sentence, based on location information of a feature value of the target word and the mapping information.

Translation method and apparatus, and translation system

A processor implemented method to update a sentence generation model includes: generating a target sentence corresponding to a source sentence using a first decoding model; calculating reward information associated with the target sentence using a second decoding model configured to generate a sentence in an order different from an order of the sentence generated by the first decoding model; and generating an updated sentence generation model by resetting a weight of respective nodes in the first decoding model based on the calculated reward information.

Method of updating sentence generation model and sentence generating apparatus

A model training method and apparatus is disclosed, where the model training method acquires a recognition result of a teacher model and a recognition result of a student model for an input sequence and trains the student model such that the recognition result of the teacher model and the recognition result of the student model are not distinguished from each other.

Model training method and apparatus

A model training method and apparatus is disclosed, where the model training method acquires first output data of a student model for first input data and second output data of a teacher model for second input data and trains the student model such that the first output data and the second output data are not distinguished from each other. The student model and the teacher model have different structures.

A sequence processing method and apparatus are provided. The sequence processing method includes determining a word of a first R-node corresponding to a root node based on an input sequence, generating first I-nodes that are connected to the first R-node and include relative position information with respect to the word of the first R-node, determining a word of a second R-node to correspond to each of the first I-nodes, and determining an output sequence corresponding to the input sequence based on the determined words.

Method and apparatus for processing sequence

A machine translation method includes using an encoder of a source language to determine a feature vector from a source sentence expressed in the source language, using an attention model of a target language to determine context information of the source sentence from the determined feature vector, and using a decoder of the target language to determine a target sentence expressed in the target language from the determined context information.

Machine translation using attention model and hypernetwork

A processor-implemented classification method includes: determining a first probability vector including a first probability, for each of a plurality of classes, resulting from a classification of an input with respect to the classes; determining, based on the determined first probability vector, whether one or more of the classes represented in the first probability vector are confusing classes; adjusting, in response to one or more of the classes being the confusing classes, the determined first probability vector based on a first probability of each of the confusing classes and a maximum value of the first probabilities; determining a second probability vector including a second probability, for each of the classes, resulting from another classification of the input with respect to the classes; and performing classification on the input based on a result of a comparison between the determined second probability vector and the adjusted first probability vector.

Apparatus and method with classification

A sentence mapping method includes obtaining a source language document in a source language and a target language document in a target language, wherein the target language document is a translation of the source language document, generating a translated document by translating the target language document into the source language, and mapping source language sentences in the source language document and target language sentences with the target language document by comparing the source language document and the translated document.

Method and apparatus with sentence mapping

A method of recognizing input data includes determining a feature vector corresponding to an ensemble model from input data, based on the ensemble model, and recognizing the input data based on the feature vector. The ensemble model includes a first model and a second model having a structure that is the same as a structure of the first model.

Recognition method and apparatus

A method and apparatus for training a recognition model and a recognition method and apparatus using the model are disclosed. The apparatus for training the model obtains an estimation hidden vector output from a hidden layer of the model in response to an estimation output vector output from the model at a previous time being input into the model at a current time, and trains the model such that the estimation hidden vector of the current time matches an answer hidden vector output from the hidden layer in response to an answer output vector, corresponding to the estimation output vector of the previous time, being input into the model at the current time.

Neural network method and apparatus

A machine translation method includes translating a source sentence using a first model, determining a back-translation probability of a translation result of the source sentence being back-translated into the source sentence using a second model, applying the back-translation probability to context information extracted from the source sentence in the first model, and retranslating the source sentence using the first model and the context information to which the back-translation probability is applied.

Machine translation method and apparatus

A translation method and apparatus may respectively perform or include: using one or more processors, plural different translation processes, in parallel, for a source sentence in a first language, including encoding, to generate respective feature vectors, the source sentence in each of two or more translation processes of the plural translation processes or the source sentence and a variation of the source sentence in respective translation processes of the plural translation processes, and decoding each of the respective feature vectors to generate respective plural candidate sentences in a second language; and selecting a final sentence in the second language from the respective plural candidate sentences in the second language.

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Suwon-si, South Korea

Hwidong Na