DCU Terminology Translation System for Medical Query Subtask at WMT14

This paper describes the Dublin City University terminology translation system used for our participation in the query translation subtask in the medical translation task in the Workshop on Statistical Machine Translation (WMT14). We deployed six different kinds of terminology extraction methods, and participated in three different tasks: FR‐EN and EN‐ FR query tasks, and the CLIR task. We obtained 36.2 BLEU points absolute for FR‐EN and 28.8 BLEU points absolute for EN‐FR tasks where we obtained the first place in both tasks. We obtained 51.8 BLEU points absolute for the CLIR task.

[1]  Philipp Koehn,et al.  Europarl: A Parallel Corpus for Statistical Machine Translation , 2005, MTSUMMIT.

[2]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[3]  Philipp Koehn,et al.  Statistical Significance Tests for Machine Translation Evaluation , 2004, EMNLP.

[4]  Masao Utiyama,et al.  Overview of the Patent Translation Task at the NTCIR-7 Workshop , 2008, NTCIR.

[5]  Robert L. Mercer,et al.  The Mathematics of Statistical Machine Translation: Parameter Estimation , 1993, CL.

[6]  Andreas Stolcke,et al.  SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[7]  Dan Klein,et al.  Learning and Inference for Hierarchically Split PCFGs , 2007, AAAI.

[8]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[9]  Dragos Ciobanu,et al.  Traditional and Emerging Use-Cases for Machine Translation , 2013 .

[10]  Miles Osborne,et al.  Statistical Machine Translation , 2010, Encyclopedia of Machine Learning and Data Mining.

[11]  Andy Way,et al.  Multi-Word Expression-Sensitive Word Alignment , 2010 .

[12]  Geoffrey E. Hinton,et al.  Zero-shot Learning with Semantic Output Codes , 2009, NIPS.

[13]  Geoffrey Zweig,et al.  Linguistic Regularities in Continuous Space Word Representations , 2013, NAACL.

[14]  John DeNero,et al.  Better Word Alignments with Supervised ITG Models , 2009, ACL.

[15]  Daniel Marcu,et al.  Statistical Phrase-Based Translation , 2003, NAACL.

[16]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[17]  Hermann Ney,et al.  Improved backing-off for M-gram language modeling , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[18]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[19]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[20]  Julian Kupiec,et al.  An Algorithm for Finding Noun Phrase Correspondences in Bilingual Corpora , 1993, ACL.

[21]  Josef van Genabith,et al.  Minimum Bayes Risk Decoding with Enlarged Hypothesis Space in System Combination , 2012, CICLing.

[22]  Jun'ichi Tsujii,et al.  Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data , 2005, HLT.

[23]  Franz Josef Och,et al.  Minimum Error Rate Training in Statistical Machine Translation , 2003, ACL.

[24]  Hermann Ney,et al.  A Systematic Comparison of Various Statistical Alignment Models , 2003, CL.

[25]  Tsuyoshi Okita.  DCU Confusion Network-based System Combination for ML 4 HMT , 2011 .

[26]  Quoc V. Le,et al.  Exploiting Similarities among Languages for Machine Translation , 2013, ArXiv.

[27]  Vysoké Učení,et al.  Statistical Language Models Based on Neural Networks , 2012 .