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Exploiting large pre-trained models for low-resource neural machine translation., , , and . EAMT, page 59-68. European Association for Machine Translation, (2023)Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences., and . WMT, page 362-370. The Association for Computer Linguistics, (2016)Automatic Acquisition of Machine Translation Resources in the Abu-MaTran Project., , , , , , , and . Proces. del Leng. Natural, (2015)Enriching a statistical machine translation system trained on small parallel corpora with rule-based bilingual phrases., , and . RANLP, page 90-96. RANLP 2011 Organising Committee, (2011)A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions., and . EACL (1), page 1063-1073. Association for Computational Linguistics, (2017)The Universitat d'Alacant hybrid machine translation system for WMT 2011., , and . WMT@EMNLP, page 457-463. Association for Computational Linguistics, (2011)Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation., , and . COLING, page 3938-3950. International Committee on Computational Linguistics, (2020)Integrating shallow-transfer rules into phrase-based statistical machine translation., , and . MTSummit, (2011)Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation., , and . Prague Bull. Math. Linguistics, (2017)Assisting non-expert speakers of under-resourced languages in assigning stems and inflectional paradigms to new word entries of morphological dictionaries., , , , , and . Lang. Resour. Evaluation, 51 (4): 989-1017 (2017)