首页> 外文会议>ACL-05; Association for Computational Linguistics Annual Meeting; 20050625-30; Ann Arbor,MI(US) >Phrase Linguistic Classification and Generalization for Improving Statistical Machine Translation
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Phrase Linguistic Classification and Generalization for Improving Statistical Machine Translation

机译:短语语言分类和泛化以提高统计机器翻译

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摘要

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed.
机译:在本文中,提出了一种将有关单词和复合动词的语言信息合并的方法,这是迈向基于语言分类短语的SMT模型的第一步。通过将这些动词结构替换为头动词的基本形式,我们可以获得更好的统计单词对齐性能,并且能够更好地估计翻译模型并将其泛化为翻译过程中看不见的动词形式。进行了英语-西班牙语对的初步实验,并详细介绍了未来的研究方向。

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