Enriching Morphologically Poor Languages for Statistical Machine Translation

Eleftherios Avramidis, Philipp Koehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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%.
Original languageEnglish
Title of host publicationProceedings of ACL-08: HLT
Place of PublicationColumbus, Ohio
PublisherAssociation for Computational Linguistics
Pages763-770
Number of pages8
Publication statusPublished - 1 Jun 2008

Fingerprint

Dive into the research topics of 'Enriching Morphologically Poor Languages for Statistical Machine Translation'. Together they form a unique fingerprint.

Cite this