PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation

Kemal Kurniawan, Lea Frermann, Philip Schulz, Trevor Cohn


Abstract
Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision. Simple ‘direct transfer’ of a learned model based on a multilingual input encoding has provided a strong benchmark. This paper presents a method for unsupervised cross-lingual transfer that improves over direct transfer systems by using their output as implicit supervision as part of self-training on unlabelled text in the target language. The method assumes minimal resources and provides maximal flexibility by (a) accepting any pre-trained arc-factored dependency parser; (b) assuming no access to source language data; (c) supporting both projective and non-projective parsing; and (d) supporting multi-source transfer. With English as the source language, we show significant improvements over state-of-the-art transfer models on both distant and nearby languages, despite our conceptually simpler approach. We provide analyses of the choice of source languages for multi-source transfer, and the advantage of non-projective parsing. Our code is available online.
Anthology ID:
2021.eacl-main.254
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2907–2918
Language:
URL:
https://aclanthology.org/2021.eacl-main.254
DOI:
10.18653/v1/2021.eacl-main.254
Bibkey:
Cite (ACL):
Kemal Kurniawan, Lea Frermann, Philip Schulz, and Trevor Cohn. 2021. PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2907–2918, Online. Association for Computational Linguistics.
Cite (Informal):
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation (Kurniawan et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.254.pdf
Code
 kmkurn/ppt-eacl2021
Data
Universal Dependencies