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Inter-language Vector Space is the advanced , neural network-based technology which lays that is the foundation for the XTM Cloud’s AI strategy of XTM Cloud, along with Neural MT. This Inter-language Vector Space is a unique mathematical algorithmic approach to the advancement of language technology based on massive neural networks. In shortBasically, it indicates the approximate closeness between distinct source and target words within a segment.

The technology is being used to enhance translators’translator, reviewers’ reviewer and post-editors’ editor productivity during their when they work in XTM Workbench.

Inter-Language Vector Space supports such these XTM Cloud-related operations as:

• autoAuto-insertion of inline tags,.

• auto-Automatic bilingual term extraction,.auto

• -Automatic subsegment matching,.

• auto-Automatic fuzzy correction.

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How does

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the Inter-Language Vector Space technology work?

Inter-Language Vector Space enables direct alignment at the word and phrase level for the translation of when a particular segment is translated in XTM Workbench. It is built on extensive resources by obtained from Google and Facebook, with XTM Cloud contributing key elements from the multilingual L10N , multilingual point of view.

This The Inter-Language Vector Space technology is AI-based technology and draws on massive Big Data resources, including the resources of all of the entire Internet and XTM’s XTM Cloud’s massive bilingual dictionaries, to calculate the probability of a particular target language word being the correct translation of a source word, for over 250 language pairs. The purpose of this technology is to aid linguists Linguists in performing simple tasks by offering algorithm-driven automation, to improve their productivity and user experience.

Manual transfer of inline elements to target segments or bilingual term extraction take an inordinate amount of time and effort, negatively impacting linguists’ Linguist productivity and creativity. Therefore, auto-For this reason, automatic insertion of inline elements and auto-automatic bilingual term extraction, supported by AI-based Inter-Language Vector Space, help eliminate the these mundane work from tasks in translation processes. This, in turn, brings quicker turnaround times, significant cost reduction and a marked increase in quality.

Finding equivalents of source language words and phrases if no dictionaries or existing translations are available is also a laborious and time-consuming activity. Since Inter-language Vector Space is based on extensive neural network analysis of the whole of the entire Internet, encompassing 150+ languages, according to this it uses a new approach, according to which language is represented as a set of relationships between words and vector space points from one word to another, resulting in producing simple answers to operationsrelationships between words, such as king is to ‘man’ as ‘woman’ is to queen. As a result, Inter-Language Vector Space is able to automatically work out relationships between how words and how they relate to one another.

It knows, for instance, that if king relates to ‘man’ and therefore can also provide , the equivalent queen word for ‘woman’ is queen. The unique point of this technology is that this can be done it can identify these relationships across different languages.

Take a look at the following Multilingual example:

王 (king in Japanese) is to ‘man’, as ‘woman’ is to 女王 (queen in Japanese).