Improving Translation via Targeted Paraphrasing

Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation that makes use of simple and inexpensive human computations by monolingual speakers in combination with machine translation. The key insight behind the process is that it is possible to spot likely translation errors with only monolingual knowledge of the target language, and it is possible to generate alternative ways to say the same thing (i.e. paraphrases) with only monolingual knowledge of the source language. Evaluations demonstrate that this approach can yield substantial improvements in translation quality.

[1]  Lynne Bowker,et al.  Bilingual concordancers and translation memories: A comparative evaluation , 2004 .

[2]  Jun Hu,et al.  Improving Arabic-Chinese Statistical Machine Translation using English as Pivot Language , 2009, WMT@EACL.

[3]  Chris Callison-Burch,et al.  Paraphrasing with Bilingual Parallel Corpora , 2005, ACL.

[4]  Nitin Madnani,et al.  TER-Plus: paraphrase, semantic, and alignment enhancements to Translation Edit Rate , 2009, Machine Translation.

[5]  David Yarowsky,et al.  Inducing Multilingual Text Analysis Tools via Robust Projection across Aligned Corpora , 2001, HLT.

[6]  Philip Resnik,et al.  Bootstrapping parsers via syntactic projection across parallel texts , 2005, Natural Language Engineering.

[7]  Andy Way,et al.  Facilitating Translation Using Source Language Paraphrase Lattices , 2010, EMNLP.

[8]  Christopher J. Dyer,et al.  The “Noisier Channel”: Translation from Morphologically Complex Languages , 2007, WMT@ACL.

[9]  Chris Callison-Burch,et al.  Improving statistical translation through editing , 2004, EAMT.

[10]  Chris Callison-Burch,et al.  Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk , 2009, EMNLP.

[11]  Smaranda Muresan,et al.  Generalizing Word Lattice Translation , 2008, ACL.

[12]  Philipp Koehn,et al.  A Web-Based Interactive Computer Aided Translation Tool , 2009, ACL.

[13]  Dafna Shahaf,et al.  Generalized Task Markets for Human and Machine Computation , 2010, AAAI.

[14]  Benjamin B. Bederson,et al.  Translation by iterative collaboration between monolingual users , 2010, HCOMP '10.

[15]  Matthew G. Snover,et al.  A Study of Translation Edit Rate with Targeted Human Annotation , 2006, AMTA.

[16]  G. Elisabeta Marai,et al.  Correcting Automatic Translations through Collaborations between MT and Monolingual Target-\-Lan\-gua\-ge Users , 2009, EACL.

[17]  Guy Lapalme,et al.  TransType2 - An Innovative Computer-Assisted Translation System , 2004, ACL.

[18]  Dan Klein,et al.  Fast Exact Inference with a Factored Model for Natural Language Parsing , 2002, NIPS.

[19]  Ralph Weischedel,et al.  A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION , 2005 .

[20]  Olivia Buzek,et al.  Error Driven Paraphrase Annotation using Mechanical Turk , 2010, Mturk@HLT-NAACL.

[21]  Anne-Marie Laurian,et al.  Machine Translation : What Type of Post-Editing on What Type of Documents for What Type of Users , 1984, ACL.

[22]  Radu Soricut,et al.  TrustRank: Inducing Trust in Automatic Translations via Ranking , 2010, ACL.

[23]  Aurélien Max,et al.  Example-Based Paraphrasing for Improved Phrase-Based Statistical Machine Translation , 2010, EMNLP.

[24]  Toru Ishida,et al.  Designing Protocols for Collaborative Translation , 2009, PRIMA.

[25]  Aurélien Max,et al.  Sub-sentencial Paraphrasing by Contextual Pivot Translation , 2009, TextInfer@ACL.

[26]  Alexander M. Fraser,et al.  A Smorgasbord of Features for Statistical Machine Translation , 2004, NAACL.

[27]  Hermann Ney,et al.  Integration of Speech to Computer-Assisted Translation Using Finite-State Automata , 2006, ACL.