@inproceedings{li-etal-2018-joint-learning,
title = "Joint Learning of {POS} and Dependencies for Multilingual {U}niversal {D}ependency Parsing",
author = "Li, Zuchao and
He, Shexia and
Zhang, Zhuosheng and
Zhao, Hai",
editor = "Zeman, Daniel and
Haji{\v{c}}, Jan",
booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K18-2006",
doi = "10.18653/v1/K18-2006",
pages = "65--73",
abstract = "This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other richresource languages. Our system achieves a macro-average of 68.31{\%} LAS F1 score, with an improvement of 2.51{\%} compared with the UDPipe.",
}
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%0 Conference Proceedings
%T Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing
%A Li, Zuchao
%A He, Shexia
%A Zhang, Zhuosheng
%A Zhao, Hai
%Y Zeman, Daniel
%Y Hajič, Jan
%S Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F li-etal-2018-joint-learning
%X This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other richresource languages. Our system achieves a macro-average of 68.31% LAS F1 score, with an improvement of 2.51% compared with the UDPipe.
%R 10.18653/v1/K18-2006
%U https://aclanthology.org/K18-2006
%U https://doi.org/10.18653/v1/K18-2006
%P 65-73
Markdown (Informal)
[Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing](https://aclanthology.org/K18-2006) (Li et al., CoNLL 2018)
ACL