Summary of the paper

Title Manual vs Automatic Bitext Extraction
Authors Aibek Makazhanov, Bagdat Myrzakhmetov and Zhenisbek Assylbekov
Abstract We compare manual and automatic approaches to the problem of extracting bitexts from the Web in the framework of a case study on building a Russian-Kazakh parallel corpus. Our findings suggest that targeted, site-specific crawling results in cleaner bitexts with a higher ratio of parallel sentences. We also find that general crawlers combined with boilerplate removal tools tend to retrieve shorter texts, as some content gets cleaned out with the markup. When it comes to sentence splitting and alignment we show that investing some effort in data pre- and post-processing as well as fiddling with off-the-shelf solutions pays a noticeable dividend. Overall we observe that, depending on the source, automatic bitext extraction methods may lack severely in coverage (retrieve fewer sentence pairs) and on average are less precise (retrieve fewer parallel sentence pairs). We conclude that if one aims at extracting high-quality bitexts for a small number of language pairs, automatic methods best be avoided, or at least used with caution.
Topics Statistical And Machine Learning Methods, Corpus (Creation, Annotation, Etc.), Other
Full paper Manual vs Automatic Bitext Extraction
Bibtex @InProceedings{MAKAZHANOV18.1097,
  author = {Aibek Makazhanov and Bagdat Myrzakhmetov and Zhenisbek Assylbekov},
  title = "{Manual vs Automatic Bitext Extraction}",
  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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