Professional human translators usually do not employ the concept of word alignments, producing translations ‘sense-forsense’ instead of ‘word-for-word’. This suggests that unalignable words may be prevalent in the parallel text used for machine translation (MT). We analyze this phenomenon in-depth for Chinese-English translation. We further propose a simple and effective method to improve automatic word alignment by pre-removing unalignable words, and show improvements on hierarchical MT systems in both translation directions. 1 Motivation It is generally acknowledged that absolute equivalence between two languages is impossible, since concept lexicalization varies across languages. Major translation theories thus argue that texts should be translated ‘sense-for-sense’ instead of ‘word-for-word’ (Nida, 1964). This suggests that unalignable words may be an issue for the parallel text used to train current statistical machine translation (SMT) systems. Although existing automatic word alignment methods have some mechanism to handle the lack of exact word-for-word alignment (e.g. null probabilities, fertility in the IBM models (Brown et al., 1993)), they may be too coarse-grained to model the ’sense-for-sense’ translations created by professional human translators.
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Analysis and Prediction of Unalignable Words in Parallel Text
Semantic Scholar · Computer Science · 2014
Abstract
Professional human translators usually do not employ the concept of word alignments, producing translations ‘sense-forsense’ instead of ‘word-for-word’. This suggests that unalignable words may be prevalent in the parallel text used for machine translation (MT). We analyze this phenomenon in-depth for Chinese-English translation. We further propose a simple and effective method to improve automatic word alignment by pre-removing unalignable words, and show improvements on hierarchical MT systems in both translation directions. 1 Motivation It is generally acknowledged that absolute equivalence between two languages is impossible, since concept lexicalization varies across languages. Major translation theories thus argue that texts should be translated ‘sense-for-sense’ instead of ‘word-for-word’ (Nida, 1964). This suggests that unalignable words may be an issue for the parallel text used to train current statistical machine translation (SMT) systems. Although existing automatic word alignment methods have some mechanism to handle the lack of exact word-for-word alignment (e.g. null probabilities, fertility in the IBM models (Brown et al., 1993)), they may be too coarse-grained to model the ’sense-for-sense’ translations created by professional human translators.