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Combining bilingual terminology mining and morphological modeling for domain adaptation in SMT.

, , and . EAMT, page 11-18. European Association for Machine Translation, (2014)

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Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation., and . CoRR, (2020)LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020., , , and . BUCC@LREC, page 49-55. European Language Resources Association, (2020)Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19., and . WMT (2), page 400-406. Association for Computational Linguistics, (2019)The LMU Munich Unsupervised Machine Translation System for WMT19., , , and . WMT (2), page 393-399. Association for Computational Linguistics, (2019)LMU Munich's Neural Machine Translation Systems at WMT 2018., , , and . WMT (shared task), page 648-654. Association for Computational Linguistics, (2018)Rich Bitext Projection Features for Parse Reranking., , and . EACL, page 282-290. The Association for Computer Linguistics, (2009)Producing Unseen Morphological Variants in Statistical Machine Translation., , , and . EACL (2), page 369-375. Association for Computational Linguistics, (2017)HimL: Health in my language., and . EAMT (Projects/Products), Baltic Journal of Modern Computing, (2016)Towards Handling Compositionality in Low-Resource Bilingual Word Induction., and . AMTA, page 89-101. Association for Machine Translation in the Americas, (2020)CUNI-LMU Submissions in WMT2016: Chimera Constrained and Beaten., , , and . WMT, page 385-390. The Association for Computer Linguistics, (2016)