The 'noisier channel'
Christopher J. Dyer · 2007
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.