We address the problem of translating from morphologically poor to morphologically rich languages by adding per-word linguistic information 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 accordingly. In experiments, we show improved performance for translating from English into Greek and Czech. For English-Greek, we reduce the error on the verb conjugation from 19% to 5.4% and noun case agreement from 9% to 6%.
REF report work on translating from poor to rich morphology, namely, English to Greek and Czech translation.
Enriching Morphologically Poor Languages for Statistical Machine Translation
Annual Meeting Of The Association For Computational Linguistics
Computer Science