Polish -English Statistical Machine Translation of Medical Texts
This work addresses the challenge of translating specialized medical documents between Polish and English, though it represents an incremental application of existing statistical machine translation methods to a new language pair and domain.
The researchers investigated how different training methods affect Polish-English statistical machine translation for medical texts, finding that specific data preparation techniques and system configurations yielded measurable improvements in translation quality across multiple evaluation metrics.
This new research explores the effects of various training methods on a Polish to English Statistical Machine Translation system for medical texts. Various elements of the EMEA parallel text corpora from the OPUS project were used as the basis for training of phrase tables and language models and for development, tuning and testing of the translation system. The BLEU, NIST, METEOR, RIBES and TER metrics have been used to evaluate the effects of various system and data preparations on translation results. Our experiments included systems that used POS tagging, factored phrase models, hierarchical models, syntactic taggers, and many different alignment methods. We also conducted a deep analysis of Polish data as preparatory work for automatic data correction such as true casing and punctuation normalization phase.