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Translation Model Adaptation Using Genre-Revealing Text Features.

, , and . DiscoMT@EMNLP, page 132-141. Association for Computational Linguistics, (2015)

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Document understanding for a broad class of documents., , and . IJDAR, 5 (1): 1-16 (2002)Symbolic-to-statistical hybridization: extending generation-heavy machine translation., , and . Machine Translation, 23 (1): 23-63 (2009)Task-based evaluation of text summarization using Relevance Prediction., , , and . Inf. Process. Manage., 43 (6): 1482-1499 (2007)A Resolution Calculus for Dynamic Semantics, and . CoRR, (2000)What does Attention in Neural Machine Translation Pay Attention to?, and . IJCNLP(1), page 30-39. Asian Federation of Natural Language Processing, (2017)Findings of the 2009 Workshop on Statistical Machine Translation., , , and . WMT@EACL, page 1-28. Association for Computational Linguistics, (2009)Findings of the 2015 Workshop on Statistical Machine Translation., , , , , , , , , and 4 other author(s). WMT@EMNLP, page 1-46. The Association for Computer Linguistics, (2015)Findings of the 2012 Workshop on Statistical Machine Translation., , , , , and . WMT@NAACL-HLT, page 10-51. The Association for Computer Linguistics, (2012)978-1-937284-20-6.Findings of the 2011 Workshop on Statistical Machine Translation., , , and . WMT@EMNLP, page 22-64. Association for Computational Linguistics, (2011)NeurAlign: Combining Word Alignments Using Neural Networks., , and . HLT/EMNLP, page 65-72. The Association for Computational Linguistics, (2005)