For languages such as English, several constituent-to-dependency conversion schemes are proposed to construct corpora for dependency parsing. It is hard to determine which scheme is better because they reflect different views of dependency analysis. We usually obtain dependency parsers of different schemes by training with the specific corpus separately. It neglects the correlations between these schemes, which can potentially benefit the parsers. In this paper, we study how these correlations influence final dependency parsing performances, by proposing a joint model which can make full use of the correlations between heterogeneous dependencies, and finally we can answer the following question: parsing heterogeneous dependencies jointly or separately, which is better? We conduct experiments with two different schemes on the Penn Treebank and the Chinese Penn Treebank respectively, arriving at the same conclusion that jointly parsing heterogeneous dependencies can give improved performances for both schemes over the individual models.
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机译：对于诸如英语之类的语言，提出了几种从成分到依存的转换方案来构造用于依存分析的语料库。很难确定哪种方案更好，因为它们反映了依赖性分析的不同观点。我们通常通过分别与特定语料库一起训练来获得不同方案的依赖解析器。它忽略了这些方案之间的相关性，这可能会使解析器受益。在本文中，我们通过提出一个可以充分利用异构依赖关系之间的关联的联合模型，研究了这些相互关系如何影响最终依赖关系解析性能，最后我们可以回答以下问题：联合或分别解析异构依赖关系，即更好的？我们分别在Penn Treebank和Chinese Penn Treebank上使用两种不同的方案进行了实验，得出的结论是，联合解析异构依赖性可以在单个模型上为这两种方案提供改进的性能。
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