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Modelling the Adjunct/Argument Distinction in Hierarchical Phrase-Based SMT

机译:在基于层次短语的SMT中对辅助词/参数区分进行建模

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We present the first application of the adjunct/argument distinction to Hierarchical Phrase-Based SMT. We use rule labelling to characterize synchronous recursion with adjuncts and arguments. Our labels are bilingual obtained from dependency annotations and extended to cover non-syntactic phrases. The label set we derive in this manner is extremely small, as it contains only thirty-six labels, and yet we find it useful to cluster these labels even further. We present a clustering method that uses label similarity based on left-hand-side/right-hand-side joint trained-model estimates. The results of initial experiments show that our model performs similarly to Hiero on in-domain French-English data.
机译:我们介绍了辅助/参数区分在基于层次短语的SMT中的首次应用。我们使用规则标签来表征带有辅助词和参数的同步递归。我们的标签是从依赖项注释中双语获取的,并扩展到涵盖非语法性短语。我们以这种方式得出的标签集非常小,因为它仅包含36个标签,但我们发现将这些标签进一步聚类很有用。我们提出一种基于左侧/右侧联合训练模型估计值使用标签相似性的聚类方法。初始实验的结果表明,我们的模型在域内法语-英语数据上的​​表现与Hiero相似。

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