Abstract
We present a phrase-based extension to memory-based machine translation. This form of examplebased 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.
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.
| Original language | English |
|---|---|
| Title of host publication | Computational Linguistics in the Netherlands 2010: Selected Papers from the Twentieth CLIN Meeting |
| Publisher | Association for Computational Linguistics |
| Publication status | Published - 2011 |
| Externally published | Yes |