Avramidis, Eleftherios

Koehn, Philipp

English

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%

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Enriching Morphologically Poor Languages for Statistical Machine Translation

Enriching Morphologically Poor Languages for Statistical Machine Translation

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