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MT Adaptation for Under-Resourced Domains - What Works and What Not.

, and . Baltic HLT, volume 247 of Frontiers in Artificial Intelligence and Applications, page 176-184. IOS Press, (2012)

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Effective Online Learning Implementation for Statistical Machine Translation., , , and . DB&IS, volume 838 of Communications in Computer and Information Science, page 302-313. Springer, (2018)Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian., , and . LREC, European Language Resources Association (ELRA), (2016)Natural Language Based Posting Account Classification., , , and . Balt. J. Mod. Comput., (2021)From Zero to Production: Baltic-Ukrainian Machine Translation Systems to Aid Refugees., and . CoRR, (2022)Language Technology Platform for Public Administration., , , , , , and . Baltic HLT, volume 328 of Frontiers in Artificial Intelligence and Applications, page 182-190. IOS Press, (2020)Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets., and . Baltic HLT, volume 328 of Frontiers in Artificial Intelligence and Applications, page 55-61. IOS Press, (2020)Dynamic Terminology Integration for COVID-19 and Other Emerging Domains., and . WMT@EMNLP, page 821-827. Association for Computational Linguistics, (2021)Tilde's Machine Translation Systems for WMT 2019., , and . WMT (2), page 327-334. Association for Computational Linguistics, (2019)What Can We Really Learn from Post-editing?, , , and . AMTA (2), page 86-91. The Association for Machine Translation in the Americas, (2016)Unsupervised Machine Translation in Real-World Scenarios., , , , , , , , and . LREC, page 3038-3047. European Language Resources Association, (2022)