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Improving statistical MT through morphological analysis
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This paper presents a new paradigm for translation from inflectionally rich languages that was used in the University of Maryland statistical machine translation system for the WMT07 Shared Task. The system is based on a hierarchical phrase-based decoder that has been augmented to translate ambiguous input given in the form of a confusion network (CN), a weighted finite state representation of a set of strings. By treating morphologically derived forms of the input sequence as possible, albeit more "costly" paths that the decoder may select, we find that significant gains (10% BLEU relative) can be attained when translating from Czech, a language with considerable inflectional complexity, into English.