Summary of the paper

Title Pre-ordering of Phrase-based Machine Translation Input in Translation Workflow
Authors Alexandru Ceausu and Sabine Hunsicker
Abstract Word reordering is a difficult task for decoders when the languages involved have a significant difference in syntax. Phrase-based statistical machine translation (PBSMT), preferred in commercial settings due to its maturity, is particularly prone to errors in long range reordering. Source sentence pre-ordering, as a pre-processing step before PBSMT, proved to be an efficient solution that can be achieved using limited resources. We propose a dependency-based pre-ordering model with parameters optimized using a reordering score to pre-order the source sentence. The source sentence is then translated using an existing phrase-based system. The proposed solution is very simple to implement. It uses a hierarchical phrase-based statistical machine translation system (HPBSMT) for pre-ordering, combined with a PBSMT system for the actual translation. We show that the system can provide alternate translations of less post-editing effort in a translation workflow with German as the source language.
Topics Parsing, Corpus (Creation, Annotation, etc.)
Full paper Pre-ordering of Phrase-based Machine Translation Input in Translation Workflow
Bibtex @InProceedings{CEAUSU14.1213,
  author = {Alexandru Ceausu and Sabine Hunsicker},
  title = {Pre-ordering of Phrase-based Machine Translation Input in Translation Workflow},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  month = {may},
  date = {26-31},
  address = {Reykjavik, Iceland},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-8-4},
  language = {english}
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