1 research outputs found
Telemedicine as a special case of Machine Translation
Machine translation is evolving quite rapidly in terms of quality. Nowadays,
we have several machine translation systems available in the web, which provide
reasonable translations. However, these systems are not perfect, and their
quality may decrease in some specific domains. This paper examines the effects
of different training methods when it comes to Polish - English Statistical
Machine Translation system used for the medical data. Numerous elements of the
EMEA parallel text corpora and not related OPUS Open Subtitles project were
used as the ground for creation of phrase tables and different language models
including the development, tuning and testing of these translation systems. The
BLEU, NIST, METEOR, and TER metrics have been used in order to evaluate the
results of various systems. Our experiments deal with the systems that include
POS tagging, factored phrase models, hierarchical models, syntactic taggers,
and other alignment methods. We also executed a deep analysis of Polish data as
preparatory work before automatized data processing such as true casing or
punctuation normalization phase. Normalized metrics was used to compare
results. Scores lower than 15% mean that Machine Translation engine is unable
to provide satisfying quality, scores greater than 30% mean that translations
should be understandable without problems and scores over 50 reflect adequate
translations. The average results of Polish to English translations scores for
BLEU, NIST, METEOR, and TER were relatively high and ranged from 70,58 to
82,72. The lowest score was 64,38. The average results ranges for English to
Polish translations were little lower (67,58 - 78,97). The real-life
implementations of presented high quality Machine Translation Systems are
anticipated in general medical practice and telemedicine