Summary of the paper

Title Word Alignment for English-Turkish Language Pair
Authors Mehmet Talha Çakmak, Süleyman Acar and Gülşen Eryiğit
Abstract Word alignment is an important step for machine translation systems. Although the alignment performance between grammatically similar languages is reported to be very high in many studies, the case is not the same for language pairs from different language families. In this study, we are focusing on English-Turkish language pairs. Turkish is a highly agglutinative language with a very productive and rich morphology whereas English has a very poor morphology when compared to this language. As a result of this, one Turkish word is usually aligned with several English words. The traditional models which use word-level alignment approaches generally fail in such circumstances. In this study, we evaluate a Giza++ system by splitting the words into their morphological units (stem and suffixes) and compare the model with the traditional one. For the first time, we evaluate the performance of our aligner on gold standard parallel sentences rather than in a real machine translation system. Our approach reduced the alignment error rate by 40% relative. Finally, a new test corpus of 300 manually aligned sentences is released together with this study.
Topics Machine Translation, SpeechToSpeech Translation, Morphology, Language modelling
Full paper Word Alignment for English-Turkish Language Pair
Bibtex @InProceedings{AKMAK12.380,
  author = {Mehmet Talha Çakmak and Süleyman Acar and Gülşen Eryiğit},
  title = {Word Alignment for English-Turkish Language Pair},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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