This paper presents a novel method to suggest long word reorderings to a phrase-based SMT decoder. We address language pairs where long reordering concentrates on few patterns, and use fuzzy chunk-based rules to predict likely reorderings for these phenomena. Then we use reordered n-gram LMs to rank the re- sulting permutations and select the n-best for translation. Finally we encode these reorder- ings by modifying selected entries of the dis- tortion cost matrix, on a per-sentence basis. In this way, we expand the search space by a much finer degree than if we simply raised the distortion limit. The proposed techniques are tested on Arabic-English and German-English using well-known SMT benchmarks.

Modified Distortion Matrices for Phrase-Based Statistical Machine Translation

Bisazza, Arianna;Federico, Marcello
2012

Abstract

This paper presents a novel method to suggest long word reorderings to a phrase-based SMT decoder. We address language pairs where long reordering concentrates on few patterns, and use fuzzy chunk-based rules to predict likely reorderings for these phenomena. Then we use reordered n-gram LMs to rank the re- sulting permutations and select the n-best for translation. Finally we encode these reorder- ings by modifying selected entries of the dis- tortion cost matrix, on a per-sentence basis. In this way, we expand the search space by a much finer degree than if we simply raised the distortion limit. The proposed techniques are tested on Arabic-English and German-English using well-known SMT benchmarks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/106202
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