Paper: Enriching Morphologically Poor Languages for Statistical Machine Translation

ACL ID P08-1087
Title Enriching Morphologically Poor Languages for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

We address the problem of translating from morphologically poor to morphologically rich languages by adding per-word linguistic in- formation to the source language. We use the syntax of the source sentence to extract information for noun cases and verb persons and annotate the corresponding words accord- ingly. In experiments, we show improved performance for translating from English into Greek and Czech. For English–Greek, we re- duce the error on the verb conjugation from 19% to 5.4% and noun case agreement from 9% to 6%.