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This paper explores the task of leveraging typology in the context ofcross-lingual dependency parsing. While this linguistic information has showngreat promise in pre-neural parsing, results for neural architectures have beenmixed. The aim of our investigation is to better understand thisstate-of-the-art. Our main findings are as follows: 1) The benefit oftypological information is derived from coarsely grouping languages intosyntactically-homogeneous clusters rather than from learning to leveragevariations along individual typological dimensions in a compositional manner;2) Typology consistent with the actual corpus statistics yields better transferperformance; 3) Typological similarity is only a rough proxy of cross-lingualtransferability with respect to parsing.

Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers