A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes learning distributed representations of words included in a word space of a first language using a learner for learning the distributed representations; classifying words included in a word space of a second language different from the first language into words common to words included in the word space of the first language and words not common to words included in the word space of the first language; and replacing distributed representations of the common words included in the word space of the second language with distributed representations of the words, corresponding to the common words, in the first language and adjusting a parameter of the learner.

A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes learning distributed representations of words included in a word space of a first language using a learner for learning the distributed representations; classifying words included in a word space of a second language different from the first language into words common to words included in the word space of the first language and words not common to words included in the word space of the first language; and replacing distributed representations of the common words included in the word space of the second language with distributed representations of the words, corresponding to the common words, in the first language and adjusting a parameter of the learner.

A method and system for automatically analyzing legal documents are provided herein. The method may include the following steps: receiving a labelled legal document and at least one unlabeled legal document, wherein the legal documents exhibit similarity in terms of table of content thereof, and wherein the labelled legal document is labelled with a plurality of labels each indicating a start point and an end point of predefined entities associated with the legal documents; converting the legal documents to respective sequences of characters; applying a global alignment sequencing process to the sequence of characters of the labelled legal document and the sequence of characters of the at least one unlabeled legal document, based on the labels; deriving pointers to the start points and end points of the predefined entities in the unlabeled legal document based on the global alignment sequencing process; and labeling the unlabeled legal document using the pointers.

A method and system for automatically analyzing legal documents are provided herein. The method may include the following steps: receiving a labelled legal document and at least one unlabeled legal document, wherein the legal documents exhibit similarity in terms of table of content thereof, and wherein the labelled legal document is labelled with a plurality of labels each indicating a start point and an end point of predefined entities associated with the legal documents; converting the legal documents to respective sequences of characters; applying a global alignment sequencing process to the sequence of characters of the labelled legal document and the sequence of characters of the at least one unlabeled legal document, based on the labels; deriving pointers to the start points and end points of the predefined entities in the unlabeled legal document based on the global alignment sequencing process; and labeling the unlabeled legal document using the pointers.

In an embodiment, the disclosure provides a programmed computer system implemented via client-server Software as a Service (SaaS) techniques that allows for machine translation of digital content. When translating digital content, linguists must translate more than just the text on the page. Formatting, for example, is a commonly used and important aspect of online content that is typically managed with tags, such as <b> for bold and <i> for italics. When linguists work, they must ensure these tags are placed accurately as part of the translation. Projecting tags accurately depends on successfully accomplishing the challenging task of word alignment. Unfortunately, if word alignment is inaccurate, it makes placing formatting tags very difficult. In an embodiment, the present disclosure provides a method of not only translating text, but also efficiently and accurately projecting tags from input text in one language to output text in another language.

A control device of voice distribution including: at least one voice processing module arranged to—receive as input an audio signal including a first vocal message, and—provide as output an audio signal including a second vocal message, the first and second vocal messages being different one from the other and the second vocal message resulting from a processing of the first vocal message; a communication module arranged to establish and simultaneously manage a wireless, bidirectional and audio link with each one of a plurality of auxiliary devices, each link being connected to the input and/or the output of at least one voice processing module.

A control device of voice distribution including: at least one voice processing module arranged to—receive as input an audio signal including a first vocal message, and—provide as output an audio signal including a second vocal message, the first and second vocal messages being different one from the other and the second vocal message resulting from a processing of the first vocal message; a communication module arranged to establish and simultaneously manage a wireless, bidirectional and audio link with each one of a plurality of auxiliary devices, each link being connected to the input and/or the output of at least one voice processing module.

Embodiments described herein provide adversarial attacks targeting the cross-lingual generalization ability of massive multilingual representations, demonstrating their effectiveness on multilingual models for natural language inference and question answering. An efficient adversarial training scheme can thus be implemented with the adversarial attacks, which takes the same number of steps as standard supervised training and show that it encourages language-invariance in representations, thereby improving both clean and robust accuracy.

Embodiments described herein provide adversarial attacks targeting the cross-lingual generalization ability of massive multilingual representations, demonstrating their effectiveness on multilingual models for natural language inference and question answering. An efficient adversarial training scheme can thus be implemented with the adversarial attacks, which takes the same number of steps as standard supervised training and show that it encourages language-invariance in representations, thereby improving both clean and robust accuracy.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of:(a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.