Polish - English Speech Statistical Machine Translation Systems for the IWSLT 2014
This work addresses translation challenges for Polish-English spoken language, but it is incremental as it builds on existing statistical machine translation methods with specific data enhancements.
The study investigated the impact of different training configurations on Polish-English statistical machine translation for spoken language, using TED and Wikipedia corpora, and found that incorporating lemma and morphological information improved translation results as measured by BLEU, NIST, METEOR, and TER metrics.
This research explores effects of various training settings between Polish and English Statistical Machine Translation systems for spoken language. Various elements of the TED parallel text corpora for the IWSLT 2014 evaluation campaign were used as the basis for training of language models, and for development, tuning and testing of the translation system as well as Wikipedia based comparable corpora prepared by us. The BLEU, NIST, METEOR and TER metrics were used to evaluate the effects of data preparations on translation results. Our experiments included systems, which use lemma and morphological information on Polish words. We also conducted a deep analysis of provided Polish data as preparatory work for the automatic data correction and cleaning phase.