Dependency treelet translation: the convergence of statistical and example-based machine-translation? |
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Authors: | Christopher Quirk Arul Menezes |
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Affiliation: | (1) Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA |
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Abstract: | We describe a novel approach to MT that combines the strengths of the two leading corpus-based approaches: Phrasal SMT and
EBMT. We use a syntactically informed decoder and reordering model based on the source dependency tree, in combination with
conventional SMT models to incorporate the power of phrasal SMT with the linguistic generality available in a parser. We show
that this approach significantly outperforms a leading string-based Phrasal SMT decoder and an EBMT system. We present results
from two radically different language pairs, and investigate the sensitivity of this approach to parse quality by using two
distinct parsers and oracle experiments. We also validate our automated bleu scores with a small human evaluation. |
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Keywords: | Example-based machine translation EBMT Statistical machine translation SMT Syntax Dependency analysis |
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