A translation device includes a storage unit configured to store a plurality of pieces of learning data, a normalized sentence learning unit configured to perform learning on the plurality of pieces of learning data by combining original text for learning and a corresponding normalized sentence for learning, a translated sentence learning unit configured to perform learning on the plurality of pieces of learning data by combining the original text for learning and a corresponding translated sentence for learning, and a model generation unit configured to generate one normalization/translation model on the basis of a result of learning by the normalized sentence learning unit and the translated sentence learning unit, in which, on at least a part of the learning data, the translated sentence learning unit performs learning after the normalized sentence learning unit performs learning.

A translation device includes a storage unit configured to store a plurality of pieces of learning data, a normalized sentence learning unit configured to perform learning on the plurality of pieces of learning data by combining original text for learning and a corresponding normalized sentence for learning, a translated sentence learning unit configured to perform learning on the plurality of pieces of learning data by combining the original text for learning and a corresponding translated sentence for learning, and a model generation unit configured to generate one normalization/translation model on the basis of a result of learning by the normalized sentence learning unit and the translated sentence learning unit, in which, on at least a part of the learning data, the translated sentence learning unit performs learning after the normalized sentence learning unit performs learning.

A method of translating instructions for operating electromechanical systems includes identifying an operating-language instruction from one or more operating-language instructions at least based on user information that includes a type of an electromechanical system and a desired operation. The method further includes determining whether one or more existing translated parameters are available to the computing device. The method also includes, in response to the one or more existing translated parameters being available to the computing device, identifying an existing translated parameter from among the one or more existing translated parameters at least based on a level of relevancy of the existing translated parameter to one or more user parameters, where the existing translated parameter has a highest level of relevancy to the one or more user parameters. The method also includes generating, by the computing device, a translated instruction from the operating-language instruction.

A method of translating instructions for operating electromechanical systems includes identifying an operating-language instruction from one or more operating-language instructions at least based on user information that includes a type of an electromechanical system and a desired operation. The method further includes determining whether one or more existing translated parameters are available to the computing device. The method also includes, in response to the one or more existing translated parameters being available to the computing device, identifying an existing translated parameter from among the one or more existing translated parameters at least based on a level of relevancy of the existing translated parameter to one or more user parameters, where the existing translated parameter has a highest level of relevancy to the one or more user parameters. The method also includes generating, by the computing device, a translated instruction from the operating-language instruction.

Technologies are provided for automated quality assessment of translations. In some embodiments, quality of a translation can be assessed by generating a machine-learning (ML) model that classifies the translation as pertaining to one of three quality categories. A first quality category can include, for example, translations that are deemed satisfactory. A second quality category can include, for example, translations that are deemed subject to edition prior to being deemed satisfactory. A third quality category can include, for example, translations that are deemed unsatisfactory. The generated ML model can then be applied to the translation and a corresponding sentence in a source language in order to classify the translation as pertaining to one of the three categories.

Systems, devices, and methods of the present invention calculate a rhetorical relationship between one or more sentences. In an example, a computer-implemented method accesses a sentence comprising a plurality of fragments. At least one fragment includes a verb and a words. Each word includes a role of the words within the fragment. Each fragment is an elementary discourse unit. The method generates a discourse tree that represents rhetorical relationships between the sentence fragments. The discourse tree includes nodes including nonterminal and terminal nodes, each nonterminal node representing a rhetorical relationship between two of the sentence fragments, and each terminal node of the nodes of the discourse tree is associated with one of the sentence fragments. The method matches each fragment that has a verb to a verb signature, thereby creating communicative discourse tree.

Systems, devices, and methods of the present invention calculate a rhetorical relationship between one or more sentences. In an example, a computer-implemented method accesses a sentence comprising a plurality of fragments. At least one fragment includes a verb and a words. Each word includes a role of the words within the fragment. Each fragment is an elementary discourse unit. The method generates a discourse tree that represents rhetorical relationships between the sentence fragments. The discourse tree includes nodes including nonterminal and terminal nodes, each nonterminal node representing a rhetorical relationship between two of the sentence fragments, and each terminal node of the nodes of the discourse tree is associated with one of the sentence fragments. The method matches each fragment that has a verb to a verb signature, thereby creating communicative discourse tree.

A method for determining an official transcription and speaker language from a plurality of transcripts of text in different languages. The method includes receiving a preselection of a plurality of different languages in which a first speaker can speak during a session of a cloud-based meeting; receiving, from a microphone, first audio content which originated from the first speaker; transcribing the first audio content of the first speaker into text of all the plurality of languages in which the first speaker can speak to generate a first plurality of text transcripts; identifying a first untranslated speech bubble as making the most sense among the first plurality of transcripts for the first speaker; and adding the first untranslated speech bubble to a master transcript for the first speaker.

A method for determining an official transcription and speaker language from a plurality of transcripts of text in different languages. The method includes receiving a preselection of a plurality of different languages in which a first speaker can speak during a session of a cloud-based meeting; receiving, from a microphone, first audio content which originated from the first speaker; transcribing the first audio content of the first speaker into text of all the plurality of languages in which the first speaker can speak to generate a first plurality of text transcripts; identifying a first untranslated speech bubble as making the most sense among the first plurality of transcripts for the first speaker; and adding the first untranslated speech bubble to a master transcript for the first speaker.

A method for managing a cloud-based meeting between participants that speak and understand different languages is disclosed. The method includes receiving, via a microphone at a first client device, first audio content in a first language preference of a first meeting participant; transcribing the first audio content into a first transcribed text by using the first language preference; receiving, from a second client device, a second language preference that is different from the first language preference; translating the first transcribed text into a second transcribed text by using the second language preference; and transmitting the first and second transcribed text to the second client device. The second client device is configured to concurrently display the first transcribed text and the second transcribed text on a display device. The second client device can also be configured to provide a second audio content from the second transcribed text.