Statistical versus neural machine translation: a case study for a medium size domain-specific bilingual corpus
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Krzysztof Jassem [1] ; Tomasz Dwojak [1]
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[1] Adam Mickiewicz University in Poznań
Adam Mickiewicz University in Poznań
Poznań, Polonia
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- Localización: Poznan Studies in Contemporary Linguistics, ISSN 1732-0747, ISSN-e 1897-7499, Vol. 55, Nº. 2, 2019 (Ejemplar dedicado a: Current state of the art in language technology for polish), págs. 491-515
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Neural Machine Translation (NMT) has recently achieved promising results for a number of translation pairs. Although the method requires larger volumes of data and more computational power than Statistical Machine Translation (SMT), it is believed to become dominant in near future. In this paper we evaluate SMT and NMT models learned on a domain-specific English-Polish corpus of a moderate size (1,200,000 segments). The experiment shows that both solutions significantly outperform a general-domain online translator. The SMT model achieves a slightly better BLEU score than the NMT model. On the other hand, the process of decoding is noticeably faster in NMT. Human evaluation carried out on a sizeable sample of translations (2,000 pairs) reveals the superiority of the NMT approach, particularly in the aspect of output fluency.
