In an example embodiment, input is received from a first user of a computer system. A text object relating to a first item from the input is created, and translated from a first language to a second language. A plurality of text objects, in the second language, having text similar to the translated text object, are located in a database, each text object comprising textual information pertaining to the first item. The plurality of text objects having text similar to the translated text are then ranked based on a comparison of the contextual information about the first item and the contextual information stored in the database for the plurality of text objects having text similar to the translated text object. At least one of the ranked text objects is translated to the first language.

Operability of head-mounted display systems is enhanced by incorporating the following: a microphone which receives an utterance input by a person and outputs voice information; a character string generation unit which generates an uttered character string by converting the voice information into a character string; a specific utterance information storage unit which stores specific utterance information that associates at least one program to be started or stopped and/or at least one operating mode to be started or stopped, with specific utterances for starting or stopping each of the programs and/or operating modes; a specific utterance extraction unit which extracts a specific utterance included in the uttered character string with reference to the specific utterance information, and generates an extracted specific utterance signal indicating the extraction result; and a control unit which starts or stops a program or an operating mode with reference to the extracted specific utterance signal.

Provided is an automated interpretation method, apparatus, and system. The automated interpretation method includes encoding a voice signal in a first language to generate a first feature vector, decoding the first feature vector to generate a first language sentence in the first language, encoding the first language sentence to generate a second feature vector with respect to a second language, decoding the second feature vector to generate a second language sentence in the second language, controlling a generating of a candidate sentence list based on any one or any combination of the first feature vector, the first language sentence, the second feature vector, and the second language sentence, and selecting, from the candidate sentence list, a final second language sentence as a translation of the voice signal.

A tool for standardized layout transformations of BIDI data exchanged between legacy and modern systems is provided. The tool retrieves client connection information from a client request for data. The tool determines, based, at least in part, on the client connection information, a client application's operating system. The tool determines whether the data requested in the client request is BIDI data. Responsive to a determination that the data requested is BIDI data, the tool initiates a layout transformation of the data requested at a single point within the database server. The tool returns transformed BIDI data to the client application.

A translation apparatus includes a translation unit which translates content of a document into a different language, a history creating unit which, in translation of the content from a first language into a second language, creates history information including a correspondence between original text in the first language and translated text in the second language, an extraction unit which, in translation of the content from the second language into another language, if content (present content) of the document in the second language is present in the history information, extracts content (absent content) that is not present in the history information, and a combining unit which combines a translation result obtained by translating the present content from the second language into the other language, with a replacement result obtained by replacing the absent content from the second language to the other language based on the history information.

Embodiments of this application include an information conversion method for translating source information. The source information is encoded to obtain a first code. A preset conversion condition is obtained. The preset conversion condition indicates a mapping relationship between the source information and a conversion result. The first code is decoded according to the source information, the preset conversion condition, and translated information to obtain target information. The target information and the source information are in different languages. Further, the translated information includes a word obtained through conversion of the source information into a language of the target information.

Embodiments of this application include an information conversion method for translating source information. The source information is encoded to obtain a first code. A preset conversion condition is obtained. The preset conversion condition indicates a mapping relationship between the source information and a conversion result. The first code is decoded according to the source information, the preset conversion condition, and translated information to obtain target information. The target information and the source information are in different languages. Further, the translated information includes a word obtained through conversion of the source information into a language of the target information.

A computing device receives a first audio waveform representing a first utterance and a second utterance. The computing device receives identity data indicating that the first utterance corresponds to a first speaker and the second utterance corresponds to a second speaker. The computing device determines, based on the first utterance, the second utterance, and the identity data, a diarization model configured to distinguish between utterances by the first speaker and utterances by the second speaker. The computing device receives, exclusively of receiving further identity data indicating a source speaker of a third utterance, a second audio waveform representing the third utterance. The computing device determines, by way of the diarization model and independently of the further identity data of the first type, the source speaker of the third utterance. The computing device updates the diarization model based on the third utterance and the determined source speaker.

A method for controlling a virtual assistant for an industrial plant includes receiving by an input interface an information request, wherein the information request comprises at least one request for receiving information about at least part of the industrial plant; determining by a control unit a model specification using the received information request; determining by a model manager a machine learning model using the model specification; and providing by the control unit a response to the information request using the determined machine learning model.

A method for controlling a virtual assistant for an industrial plant includes receiving by an input interface an information request, wherein the information request comprises at least one request for receiving information about at least part of the industrial plant; determining by a control unit a model specification using the received information request; determining by a model manager a machine learning model using the model specification; and providing by the control unit a response to the information request using the determined machine learning model.