Extending Memory-Based Machine Translation to Phrases

Abstract

We present a phrase-based extension to memory-based machine translation. This form of example-based machine translation employs lazy-learning classifiers to translate fragments of the source sentence to fragments of the target sentence. Source-side fragments consist of variable-length phrases in a local context of neighboring words, translated by the classifier to a target-language phrase. We compare three methods of phrase extraction, and present a new decoder that reassembles the translated fragments into one final translation. Results show that one of the proposed phrase-extraction methods—the one used in Moses—leads to a translation system that outperforms context-sensitive word-based approaches. The differences, however, are small, arguably because the word-based approaches already capture phrasal context implicitly due to their source-side and target-side context sensitivity.