Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge

Lauriane Aufrant (1, 2) , Guillaume Wisniewski (3, 4, 2) , François Yvon (2)

1 DGA - Délégation générale de l'armement
2 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
3 Université Paris-Saclay
4 UP11 - Université Paris-Sud - Paris 11

This paper studies cross-lingual transfer for dependency parsing, focusing on very low-resource settings where delexicalized transfer is the only fully automatic option. We show how to boost parsing performance by rewriting the source sentences so as to better match the linguistic regularities of the target language. We contrast a data-driven approach with an approach relying on linguistically motivated rules automatically extracted from the World Atlas of Language Structures. Our findings are backed up by experiments involving 40 languages. They show that both approaches greatly outperform the baseline, the knowledge-driven method yielding the best accuracies, with average improvements of +2.9 UAS, and up to +90 UAS (absolute) on some frequent PoS configurations.

Domaines: Informatique et langage [cs.CL]

Lauriane Aufrant, Guillaume Wisniewski, François Yvon. Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge. COLING 2016, the 26th International Conference on Computational Linguistics, 2016, Osaka, Japan. pp.119--130. ⟨hal-01592335⟩