This paper presents a comparative study of two approaches to
statistical machine translation (SMT) and their application to
a task of English-to-Latvian translation, which is still an open
research line in the field of automatic translation.
We consider a state-of-the-art phrase-based SMT and an
alternative N-gram-based SMT systems. The major differences
between these two approaches lie in the distinct representations
of bilingual units, which are the components of the
bilingual model driving translation process and in the statistical
modeling of the translation context.
Latvian being a rather free word order language implies
additional difficulties to the translation process. We contrast
different reordering models and investigate how well they
deal with the word ordering issue.
Moving beyond automatic scores of translation quality
that are classically presented in MT research papers, we contribute
presenting a manual error analysis of MT systems output
that helps to shed light on advantages and disadvantages
of the SMT systems under consideration and identify the most
prominent source of errors typical for both SMT systems.
CitationKhalilov, M. [et al.]. English-Latvian SMT: the challenge of translating into a free word order language. A: International Workshop on Spoken Languages Technologies for Under-resourced Languages. "The second International Workshop on Spoken Languages Technologies for Under-Resourced Languages". Penang: 2010, p. 87-94.
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