Say It Right: AI Neural Machine Translation Empowers New Speakers to Revitalize Lemko

Artificial-intelligence-powered neural machine translation might soon resuscitate endangered languages by empowering new speakers to communicate in real time using sentences quantifiably closer to the literary norm than those of native speakers, and starting from day one of their language reclamation journey. While Silicon Valley has been investing enormous resources into neural translation technology capable of superhuman speed and accuracy for the world’s most widely used languages, 98% have been left behind, for want of corpora: neural machine translation models train on millions of words of bilingual text, which simply do not exist for most languages, and cost upwards of a hundred thousand United States dollars per tongue to assemble.

For low-resource languages, there is a more resourceful approach, if not a more effective one: transfer learning, which enables lower-resource languages to benefit from achievements among higher-resource ones. In this experiment, Google’s English-Polish neural translation service was coupled with my classical, rule-based engine to translate from English into the endangered, low-resource, East Slavic language of Lemko. The system achieved a bilingual evaluation understudy (BLEU) quality score of 6.28, several times better than Google Translate’s English to Standard Ukrainian (BLEU 2.17), Russian (BLEU 1.10), and Polish (BLEU 1.70) services. Finally, the fruit of this experiment, the world’s first English to Lemko translation service, was made available at the web address www.LemkoTran.com to empower new speakers to revitalize their language.

New speakers are key to language revitalization, and the power to “say it right” in Lemko is now at their fingertips.

1. 1.

   Disclosure: I work as a paid Russian, Polish, and Ukrainian linguist and translation quality control specialist for the Google Translate project; headquarters are in San Francisco.

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   Disclosure: the event was sponsored by the Carpatho-Rusyn Society (Pennsylvania), and I was paid by the University of Pittsburgh for my presentation.

I would like to thank my colleague Ming Qian of Peraton Labs for inspiring me to conduct this experiment, and Brian Stensrud of Soar Technology, Inc. for introducing us, as well as his encouragement.

I would also like to thank my friend Corinna Caudill for her encouragement and personal interest in the project, as well as for introducing me to Carpatho-Rusyn Society President Maryann Sivak of the University of Pittsburgh, whom I would like to thank for the opportunity to present my work.

I would also like to thank Maria Silvestri of the John and Helen Timo Foundation for conducting interviews with Lemko native speakers and donating the transcripts and my translations of them to research and development.

I would like to thank Achim Rabus of the University of Freiburg and Yves Scherrer of the University of Helsinki for their interest in the project and ideas.

I would also like to thank Myhal’ Lŷžečko of the minority-language technology blog InterFyisa for his early interest in the project and community outreach.

I would also like to thank fellow son of Zahoczewie Marko Łyszyk for his interest in the project and community outreach.

Finally, I would like to thank my co-author and Antech Systems Inc. colleague Tom Dobry for his encouragement and guidance.

Authors and Affiliations

1. LemkoTran.com, Edgewater, MD, 21037, USA

   Petro Orynycz

Corresponding author

Correspondence to Petro Orynycz .

Editors and Affiliations

1. Siemens (United States), Princeton, NJ, USA

   Helmut Degen

2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

   Stavroula Ntoa

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