Synthetic Treebanking for Cross-Lingual Dependency Parsing

TY - JOUR

T1 - Synthetic Treebanking for Cross-Lingual Dependency Parsing

AU - Agic, Zeljko

AU - Tiedemann, Jörg

PY - 2016/1/1

Y1 - 2016/1/1

N2 - How do we parse the languages for which no treebanks are available? This contribution addresses the cross-lingual viewpoint on statistical dependency parsing, in which we attempt to make use of resource-rich source language treebanks to build and adapt models for the under-resourced target languages. We outline the benefits, and indicate the drawbacks of the current major approaches. We emphasize synthetic treebanking: the automatic creation of target language treebanks by means of annotation projection and machine translation. We present competitive results in cross-lingual dependency parsing using a combination of various techniques that contribute to the overall success of the method. We further include a detailed discussion about the impact of part-of-speech label accuracy on parsing results that provide guidance in practical applications of cross-lingual methods for truly under-resourced languages.

AB - How do we parse the languages for which no treebanks are available? This contribution addresses the cross-lingual viewpoint on statistical dependency parsing, in which we attempt to make use of resource-rich source language treebanks to build and adapt models for the under-resourced target languages. We outline the benefits, and indicate the drawbacks of the current major approaches. We emphasize synthetic treebanking: the automatic creation of target language treebanks by means of annotation projection and machine translation. We present competitive results in cross-lingual dependency parsing using a combination of various techniques that contribute to the overall success of the method. We further include a detailed discussion about the impact of part-of-speech label accuracy on parsing results that provide guidance in practical applications of cross-lingual methods for truly under-resourced languages.

U2 - 10.1613/jair.4785

DO - 10.1613/jair.4785

M3 - Journal article

SN - 1076-9757

VL - 55

SP - 209

EP - 248

JO - Journal of Artificial Intelligence Research

JF - Journal of Artificial Intelligence Research

ER -