Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora

Current phrase-based SMT systems perform poorly when using small training sets. This is a consequence of unreliable translation estimates and low coverage over source and target phrases. This paper presents a method which alleviates this problem by exploiting multiple translations of the same source phrase. Central to our approach is triangulation, the process of translating from a source to a target language via an intermediate third language. This allows the use of a much wider range of parallel corpora for training, and can be combined with a standard phrase-table using conventional smoothing methods. Experimental results demonstrate BLEU improvements for triangulated models over a standard phrase-based system.

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

[2]  William A. Gale,et al.  Good-Turing Frequency Estimation Without Tears , 1995, J. Quant. Linguistics.

[3]  Hermann Ney,et al.  Improved Alignment Models for Statistical Machine Translation , 1999, EMNLP.

[4]  Mark Sanderson,et al.  Improving cross language retrieval with triangulated translation , 2001, SIGIR '01.

[5]  Hermann Ney,et al.  Statistical multi-source translation , 2001, MTSUMMIT.

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

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

[8]  Philipp Koehn,et al.  Noun phrase translation , 2003 .

[9]  Chris Callison-Burch,et al.  Bootstrapping Parallel Corpora , 2003, ParallelTexts@NAACL-HLT.

[10]  Noah A. Smith,et al.  The Web as a Parallel Corpus , 2003, CL.

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

[12]  Sergei Nirenburg,et al.  The Proper Place of Men and Machines in Language Translation , 2003 .

[13]  Hermann Ney,et al.  Improvements in Phrase-Based Statistical Machine Translation , 2004, NAACL.

[14]  MARTIN KAY.  The Proper Place of Men and Machines in Language Translation , 2004, Machine Translation.

[15]  Andreas Eisele.  First Steps towards Multi-Engine Machine Translation , 2005, ParallelText@ACL.

[16]  Roland Kuhn,et al.  Phrasetable Smoothing for Statistical Machine Translation , 2006, EMNLP.

[17]  Hermann Ney,et al.  Computing Consensus Translation for Multiple Machine Translation Systems Using Enhanced Hypothesis Alignment , 2006, EACL.

[18]  Philipp Koehn,et al.  Improved Statistical Machine Translation Using Paraphrases , 2006, NAACL.

[19]  Hitoshi Isahara,et al.  A Comparison of Pivot Methods for Phrase-Based Statistical Machine Translation , 2007, NAACL.