Summary of the paper

Title Bootstrapping a Hybrid MT System to a New Language Pair
Authors João António Rodrigues, Nuno Rendeiro, Andreia Querido, Sanja Štajner and António Branco
Abstract The usual concern when opting for a rule-based or a hybrid machine translation (MT) system is how much effort is required to adapt the system to a different language pair or a new domain. In this paper, we describe a way of adapting an existing hybrid MT system to a new language pair, and show that such a system can outperform a standard phrase-based statistical machine translation system with an average of 10 persons/month of work. This is specifically important in the case of domain-specific MT for which there is not enough parallel data for training a statistical machine translation system.
Topics Tools, Systems, Applications, Machine Translation, SpeechToSpeech Translation, Statistical and Machine Learning Methods
Full paper Bootstrapping a Hybrid MT System to a New Language Pair
Bibtex @InProceedings{ANTNIORODRIGUES16.556,
  author = {João António Rodrigues and Nuno Rendeiro and Andreia Querido and Sanja Štajner and António Branco},
  title = {Bootstrapping a Hybrid MT System to a New Language Pair},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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