@InProceedings{silvestrebaquero-mitkov:2017:HiT-IT,
  author    = {Silvestre Baquero, Andrea  and  Mitkov, Ruslan},
  title     = {Translation Memory Systems Have a Long Way to Go},
  booktitle = {Proceedings of the Workshop Human-Informed Translation and Interpreting Technology},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {Association for Computational Linguistics, Shoumen, Bulgaria},
  pages     = {44--51},
  abstract  = {The TM memory systems changed the work of translators and now the translators
	not benefiting from these tools are a tiny minority. These tools operate on
	fuzzy (surface) matching mostly and cannot benefit from already translated
	texts which are synonymous to (or paraphrased versions of) the text to be
	translated. The match score is mostly based on character-string similarity,
	calculated through Levenshtein distance. The TM tools have difficulties with
	detecting similarities even in sentences which represent a minor revision of
	sentences already available in the translation memory. This shortcoming of the
	current TM systems was the subject of the present study and was empirically
	proven in the experiments we conducted. To this end, we compiled a small
	translation memory (English-Spanish) and applied several lexical and syntactic
	transformation rules to the source sentences with both English and Spanish
	being the source language.
	The results of this study show that current TM systems have a long way to go
	and highlight the need for TM systems equipped with NLP capabilities which will
	offer the translator the advantage of he/she not having to translate a sentence
	again if an almost identical sentence has already been already translated.},
  url       = {https://doi.org/10.26615/978-954-452-042-7_006}
}