MT Adaptation for Under-Resourced Domains - What Works and What Not
暂无分享,去创建一个
[1] Tatiana Gornostay,et al. LetsMT! - Online Platform for Sharing Training Data and Building User Tailored Machine Translation , 2010, Baltic HLT.
[2] Alon Lavie,et al. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments , 2005, IEEvaluation@ACL.
[3] Philipp Koehn,et al. Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.
[4] George R. Doddington,et al. Automatic Evaluation of Machine Translation Quality Using N-gram Co-Occurrence Statistics , 2002 .
[5] Philipp Koehn,et al. Experiments in Domain Adaptation for Statistical Machine Translation , 2007, WMT@ACL.
[6] Marcin Junczys-Dowmunt. 16th Annual Conference of the European Association for Machine Translation (EAMT) , 2012 .
[7] Nikola Ljubešić,et al. Term Extraction, Tagging, and Mapping Tools for Under-Resourced Languages , 2012 .
[8] Barry Haddow,et al. Improved Minimum Error Rate Training in Moses , 2009, Prague Bull. Math. Linguistics.
[9] Matthew G. Snover,et al. A Study of Translation Edit Rate with Targeted Human Annotation , 2006, AMTA.
[10] Marcis Pinnis. Latvian and Lithuanian Named Entity Recognition with TildeNER , 2012, LREC.
[11] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.
[12] Sabine Hunsicker,et al. Hybrid Parallel Sentence Mining from Comparable Corpora , 2012, EAMT.
[13] William D. Lewis,et al. Achieving Domain Specificity in SMT without Overt Siloing , 2010, LREC.
[14] Karen Spärck Jones. A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.
[15] Ralph Weischedel,et al. A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION , 2005 .