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Enriching Morphologically Poor Languages for Statistical Machine Translation

机译:丰富形态学差的语言以进行统计机器翻译

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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%.
机译:我们通过在源语言中添加每个单词的语言信息来解决从形态不佳的语言到形态丰富的语言的翻译问题。我们使用源句子的语法来提取名词格和动词人的信息,并相应地注释相应的单词。在实验中,我们显示了从英语到希腊语和捷克语翻译的性能提高。对于英语-希腊语,我们将动词变位的错误从19%减少到5.4%,名词大小写一致性从9%减少到6%。

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