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

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Authors Adam Fisch, Jiang Guo, Regina Barzilay
Journal/Conference Name IJCNLP 2019 11
Paper Category
Paper Abstract This paper explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of our investigation is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing.
Date of publication 2019
Code Programming Language Python

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