Summary of the paper

Title Large SMT Data-sets Extracted from Wikipedia
Authors Dan Tufiș
Abstract The article presents experiments on mining Wikipedia for extracting SMT useful sentence pairs in three language pairs. Each extracted sentence pair is associated with a cross-lingual lexical similarity score based on which, several evaluations have been conducted to estimate the similarity thresholds which allow the extraction of the most useful data for training three-language pairs SMT systems. The experiments showed that for a similarity score higher than 0.7 all sentence pairs in the three language pairs were fully parallel. However, including in the training sets less parallel sentence pairs (that is with a lower similarity score) showed significant improvements in the translation quality (BLEU-based evaluations). The optimized SMT systems were evaluated on unseen test-sets also extracted from Wikipedia. As one of the main goals of our work was to help Wikipedia contributors to translate (with as little post editing as possible) new articles from major languages into less resourced languages and vice-versa, we call this type of translation experiments “in-genre” translation. As in the case of “in-domain” translation, our evaluations showed that using only “in-genre” training data for translating same genre new texts is better than mixing the training data with “out-of-genre” (even) parallel texts.
Topics Machine Translation, SpeechToSpeech Translation, Validation of LRs
Full paper Large SMT Data-sets Extracted from Wikipedia
Bibtex @InProceedings{TUFI14.103,
  author = {Dan Tufiș},
  title = {Large SMT Data-sets Extracted from Wikipedia},
  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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