When the global pandemic hit the world in 2020, TAUS created a starter kit in several languages to train high-quality translation models customized for the pandemic domain. SYSTRAN, a leading AI-based translation technology company, partnered with TAUS to use these datasets to produce twelve translation models for English to/from French, Spanish, German, Italian, Chinese and Russian and make them available on SYSTRAN Marketplace where NMT models are offered to a network of language experts to train models in any language pair and domain.
After the training with the TAUS Corona datasets, the SYSTRAN engines improved on average 18% across all twelve language pairs compared to the SYSTRAN baseline engines.
Şölen is the Head of Digital Marketing at TAUS where she leads digital growth strategies with a focus on generating compelling results via search engine optimization, effective inbound content and social media with over seven years of experience in related fields. She holds BAs in Translation Studies and Brand Communication from Istanbul University in addition to an MA in European Studies: Identity and Integration from the University of Amsterdam. After gaining experience as a transcreator for marketing content, she worked in business development for a mobile app and content marketing before joining TAUS in 2017. She believes in keeping up with modern digital trends and the power of engaging content. She also writes regularly for the TAUS Blog/Reports and manages several social media accounts she created on topics of personal interest with over 100K followers.
TAUS provided 172.980 segments of training data in French-German language pair, in a very specific area of the broadly legal domain for Custom MT, one of the latest and leading MT services companies delivering affordable machine translation engine training, evaluation, and integration.
Online machine translation engines provide easy access to high-quality machine translations. They are optimized for content like news articles and social media posts that users of online platforms frequently translate.
Finding high-quality data for MT training has always been a challenge on the path to generating high-performing MT output. The challenge increases when the language pairs are rare or when training data in a lesser-known domain is needed.