Shi, Huang, and Lee (2017) obtained state-of-the-art results for English and
Chinese dependency parsing by combining dynamic-programming implementations of
transition-based dependency parsers with a minimal set of bidirectional LSTM
features. However, their results were limited to projective parsing. In this
paper, we extend their approach to support non-projectivity by providing the
first practical implementation of the MH_4 algorithm, an $O(n^4)$ mildly
nonprojective dynamic-programming parser with very high coverage on
non-projective treebanks. To make MH_4 compatible with minimal transition-based
feature sets, we introduce a transition-based interpretation of it in which
parser items are mapped to sequences of transitions. We thus obtain the first
implementation of global decoding for non-projective transition-based parsing,
and demonstrate empirically that it is more effective than its projective
counterpart in parsing a number of highly non-projective languages

通过将转移型句法分析器的动态规划实现与最小集的双向 LSTM 功能相结合，Shi、Huang 和 Lee (2017) 获得了英语和汉语依存句法分析最先进的结果，但受限于投影分析。在本文中，我们通过提供 MH_4 算法的第一个实际实现，扩展了他们的方法以支持非投影性，在非投影性树库上具有非常高的覆盖率，并简化该算法以适应最少的转移型特征集。我们因此获得了非投影性转移型分析的全局解码的第一个实现，并证明它在解析一些高度非投影性的语言方面比其投影对应物更有效。