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A Phrase Orientation Model for Hierarchical Machine Translation.

, , , and . WMT@ACL, page 452-463. The Association for Computer Linguistics, (2013)

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Phrase Model Training for Statistical Machine Translation with Word Lattices of Preprocessing Alternatives., and . WMT@NAACL-HLT, page 450-459. The Association for Computer Linguistics, (2012)978-1-937284-20-6.The RWTH Aachen machine translation systems for IWSLT 2013., , , , , , and . IWSLT (Evaluation Campaign), (2013)The RWTH Aachen machine translation systems for IWSLT 2014., , , and . IWSLT (Evaluation Campaign), (2014)Training Phrase Translation Models with Leaving-One-Out., , and . ACL, page 475-484. The Association for Computer Linguistics, (2010)Neural Machine Translation Models Can Learn to be Few-shot Learners., , , , and . CoRR, (2023)A Comparative Study on Vocabulary Reduction for Phrase Table Smoothing., , , and . CoRR, (2019)Length-Incremental Phrase Training for SMT., and . WMT@ACL, page 309-319. The Association for Computer Linguistics, (2013)The RWTH Aachen German-English Machine Translation System for WMT 2015., , , and . WMT@EMNLP, page 158-163. The Association for Computer Linguistics, (2015)EU-BRIDGE MT: text translation of talks in the EU-BRIDGE project., , , , , , , , , and 5 other author(s). IWSLT (Evaluation Campaign), (2013)End-to-End Neural Word Alignment Outperforms GIZA++., , and . ACL, page 1605-1617. Association for Computational Linguistics, (2020)