Summary of the paper

Title Evaluating Domain Adaptation for Machine Translation Across Scenarios
Authors Thierry Etchegoyhen, Anna Fernández Torné, Andoni Azpeitia, Eva Martínez Garcia and Anna Matamala
Abstract We present an evaluation of the benefits of domain adaptation for machine translation, on three separate domains and language pairs, with varying degrees of domain specificity and amounts of available training data. Domain-adapted statistical and neural machine translation systems are compared to each other and to generic online systems, thus providing an evaluation of the main options in terms of machine translation. Alongside automated translation metrics, we present experimental results involving professional translators, in terms of quality assessment, subjective evaluations of the task and post-editing productivity measurements. The results we present quantify the clear advantages of domain adaptation for machine translation, with marked impacts for domains with higher specificity. Additionally, the results of the experiments show domain-adapted neural machine translation systems to be the optimal choice overall.
Topics Other, Parsing, Machine Translation, Speechtospeech Translation
Full paper Evaluating Domain Adaptation for Machine Translation Across Scenarios
Bibtex @InProceedings{ETCHEGOYHEN18.568,
  author = {Thierry Etchegoyhen and Anna Fernández Torné and Andoni Azpeitia and Eva Martínez Garcia and Anna Matamala},
  title = "{Evaluating Domain Adaptation for Machine Translation Across Scenarios}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
  }
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