Summary of the paper

Title Phrase-Based Machine Translation based on Simulated Annealing
Authors Caroline Lavecchia, David Langlois and Kamel Smaïli
Abstract In this paper, we propose a new phrase-based translation model based on inter-lingual triggers. The originality of our method is double. First we identify common source phrases. Then we use inter-lingual triggers in order to retrieve their translations. Furthermore, we consider the way of extracting phrase translations as an optimization issue. For that we use simulated annealing algorithm to find out the best phrase translations among all those determined by inter-lingual triggers. The best phrases are those which improve the translation quality in terms of Bleu score. Tests are achieved on movie subtitle corpora. They show that our phrase-based machine translation (PBMT) system outperforms a state-of-the-art PBMT system by almost 7 points.
Language Multiple languages
Topics Machine Translation, SpeechToSpeech Translation, Language modelling, Statistical methods
Full paper Phrase-Based Machine Translation based on Simulated Annealing
Slides Phrase-Based Machine Translation based on Simulated Annealing
Bibtex @InProceedings{LAVECCHIA08.791,
  author = {Caroline Lavecchia, David Langlois and Kamel Smaïli},
  title = {Phrase-Based Machine Translation based on Simulated Annealing},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  month = {may},
  date = {28-30},
  address = {Marrakech, Morocco},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-4-0},
  note = {http://www.lrec-conf.org/proceedings/lrec2008/},
  language = {english}
  }

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