mlsanityData to 7 Oct 2026

From Zero to Production: Baltic-Ukrainian Machine Translation Systems to Aid Refugees

arXiv 2209.14142 ↗First posted 28 Sep 2022cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we examine the development and usage of six low-resource machine translation systems translating between the Ukrainian language and each of the official languages of the Baltic states. We developed these systems in reaction to the escalating Ukrainian refugee crisis caused by the Russian military aggression in Ukraine in the hope that they might be helpful for refugees and public administrations. Now, two months after MT systems were made public, we analyze their usage patterns and statistics. Our findings show that the Latvian-Ukrainian and Lithuanian-Ukrainian systems are integrated into the public services of Baltic states, leading to more than 127 million translated sentences for the Lithuanian-Ukrainian system. Motivated by these findings, we further enhance our MT systems by better Ukrainian toponym translation and publish an improved version of the Lithuanian-Ukrainian system.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 28 Sep 2022.

Builds on

14 of 29 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  3. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  4. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  5. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
  6. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
  7. 2018Marian: Fast Neural Machine Translation in C++cited by 202
  8. 2018Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translationcited by 181
  9. 2021To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translationcited by 161
  10. 2020The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MTcited by 127
  11. 2018Rapid Adaptation of Neural Machine Translation to New Languagescited by 82
  12. 2021Facilitating Terminology Translation with Target Lemma Annotationscited by 19
2 more
  1. 2020Exploring Benefits of Transfer Learning in Neural Machine Translationcited by 5
  2. 2021Dynamic Terminology Integration for COVID-19 and other Emerging Domainscited by 3

Built on by

Not cited yet.