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AppTek Launches New Metadata-Informed Neural Machine Translation System for Enterprises; Expands MT Language and Dialect CoverageNew state-of-the-art system offers enterprise customers and translation professionals with advanced customization options for multi-domain, multi-dialect, multi-genre translations, which boost accuracy and further accelerate translation and localization workflows. MCLEAN, Va., April 14, 2022 /PRNewswire/ -- AppTek, a leader in Artificial Intelligence (AI) and Machine Learning (ML) for Automatic Speech Recognition (ASR), Neural Machine Translation (NMT), Natural Language Processing / Understanding (NLP/U) and Text-to-Speech (TTS) technologies, today announced the release of its new neural machine translation system that incorporates metadata as inputs used to customize the MT output and empower localization professionals with more accurate user-influenced machine translations. Additionally, the company expanded its core machine translation platform to support hundreds of language and dialect pairs. AppTek's new meta-aware NMT system is changing the paradigm of how professional translators work with machine translation output. Up until today, most off-the-shelf MT systems have functioned inside a "black box" where source language text is formulated into text of a target language with no or limited awareness of the surrounding context or the domain or topic of the source text, and with limited control of the resulting output. Traditionally, enterprises would need to train, deploy and maintain multiple MT systems to account for translation tasks that differ in aspects such as language, dialect, domain, topic, and more, at the risk of high deployment costs and overfitting models. With AppTek's new metadata informed NMT platform, enterprise customers can now access a single NMT system with multi-domain, multi-genre, multi-dialect content which increases the quality and adaptability of the system. By feeding additional metadata into the system, they gain more control of the MT output and can enable translators to simply "flip the switch" to the desired customized translation through relevant functionality in the user interface of the editing tools professionals work with. Examples of MT output customization achieved with using additional metadata include:
AppTek's metadata-informed MT technology is now available for translation from English to selected European languages and their varieties, with more language pairs coming soon. The system can be customized and adapted to the needs of enterprise customers by utilizing existing parallel domain-specific translation corpora found inside company archives. "As the demand for content localization continues to skyrocket, enterprises need to continue to innovate and find new ways to further accelerate production workflows," said Kyle Maddock, SVP Marketing at AppTek. "Our metadata-informed MT system has been specifically designed with translation professionals in mind, by providing them with more control over the MT output which can further speed up the localization process." In addition to its metadata-informed NMT system, AppTek has also expanded its core MT platform to cover an extensive list of languages and dialects including the addition of Indic and Slavic languages. It now supports Afrikaans, Albanian, Amharic, Arabic (multi-dialect), Armenian, Azerbaijani, Bengali, Belorussian, Bosnian, Bulgarian, Catalan, Chinese (multi-dialect), Croatian, Czech, Danish, Dari, Dutch, English (multi-dialect), Estonian, Farsi, Finnish, French (multi-dialect), Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Kyrgyz, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Marathi, Mongolian, Norwegian, Pashto, Polish, Portuguese (multi-dialect), Punjabi, Romanian, Russian, Serbian, Slovak, Slovenian, Somali, Spanish (multi-dialect), Swedish, Tagalog, Tamil, Telugu, Tigrinya, Thai, Turkish, Turkmen, Ukrainian, Urdu and Uzbek. For more information, visit www.apptek.com. About AppTek Media Contact:
SOURCE AppTek
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