@inproceedings{tiedemann-2017-cross,
title = "Cross-lingual dependency parsing for closely related languages - {H}elsinki{'}s submission to {V}ar{D}ial 2017",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1216",
doi = "10.18653/v1/W17-1216",
pages = "131--136",
abstract = "This paper describes the submission from the University of Helsinki to the shared task on cross-lingual dependency parsing at VarDial 2017. We present work on annotation projection and treebank translation that gave good results for all three target languages in the test set. In particular, Slovak seems to work well with information coming from the Czech treebank, which is in line with related work. The attachment scores for cross-lingual models even surpass the fully supervised models trained on the target language treebank. Croatian is the most difficult language in the test set and the improvements over the baseline are rather modest. Norwegian works best with information coming from Swedish whereas Danish contributes surprisingly little.",
}
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%0 Conference Proceedings
%T Cross-lingual dependency parsing for closely related languages - Helsinki’s submission to VarDial 2017
%A Tiedemann, Jörg
%S Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)
%D 2017
%8 apr
%I Association for Computational Linguistics
%C Valencia, Spain
%F tiedemann-2017-cross
%X This paper describes the submission from the University of Helsinki to the shared task on cross-lingual dependency parsing at VarDial 2017. We present work on annotation projection and treebank translation that gave good results for all three target languages in the test set. In particular, Slovak seems to work well with information coming from the Czech treebank, which is in line with related work. The attachment scores for cross-lingual models even surpass the fully supervised models trained on the target language treebank. Croatian is the most difficult language in the test set and the improvements over the baseline are rather modest. Norwegian works best with information coming from Swedish whereas Danish contributes surprisingly little.
%R 10.18653/v1/W17-1216
%U https://aclanthology.org/W17-1216
%U https://doi.org/10.18653/v1/W17-1216
%P 131-136
Markdown (Informal)
[Cross-lingual dependency parsing for closely related languages - Helsinki’s submission to VarDial 2017](https://aclanthology.org/W17-1216) (Tiedemann, 2017)
ACL