XL8 Reveals New Context Awareness Language Pairs

XL8, a Silicon Valley tech company that provides AI-powered Machine Translation technology optimized for media content, has announced its newest sets of Context Awareness (CA) language pairs designed to increase the translation and subtitling accuracy for localization service providers (LSP).

XL8’s language pairs have been found to deliver new workflow efficiencies for LSPs in terms of less post-editing work and a 32% reduction in project delivery times. These newest models bring the company’s total number of context-aware language pairs to 40. By the end of 2022, all of XL8’s 73 engines will be context-aware.

XL8’s application of Context Awareness in its Machine Translation technology enhances the immersive experiences for audiences watching live or pre-recorded streaming or broadcast content. XL8’s Context Awareness engine does what was previously the sole domain of humans, accurately considering the context of a conversation and evaluating the subtle differences of gender, slang, formalities, multiple word meanings, and other language intricacies.

XL8’s Context Awareness technology focuses on “source language to source language” to create different and specific language pairs and, as a result, achieve higher levels of accuracy.

A third-party committee of content localization service providers tested the new models for translating from English to Latin Spanish using several categories of programming (e.g., sci-fi, comedy, food, travel, drama). The tests were conducted with and without the XL8 Context Awareness model applied. While both sets performed well, the accuracy of XL8's CA model averaged 95.5% and the normal model average was 91.2% (a percentage change of +4.3%.)

Overall, the Context Awareness model was more accurate regarding gender and formality consistency among multiple subtitles. While both performed well at providing coherent sentences, even when faced with misspelled words or odd phrasings, the Context Awareness model was more accurate with certain categories like food, where dishes were described in extreme detail with long lists of ingredients.

Linguists involved with the testing have regularly noted the improvements in their performance and accuracy by using the XL8 technology.

The XL8 models also significantly improve project delivery times, especially important when LSPs need to deliver content in multiple formats and languages to an increasing number of platforms and viewers worldwide in less time and with fewer resources.

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