@inproceedings{attardi-etal-2020-linear,
title = "Linear Neural Parsing and Hybrid Enhancement for Enhanced {U}niversal {D}ependencies",
author = "Attardi, Giuseppe and
Sartiano, Daniele and
Simi, Maria",
editor = "Bouma, Gosse and
Matsumoto, Yuji and
Oepen, Stephan and
Sagae, Kenji and
Seddah, Djam{\'e} and
Sun, Weiwei and
S{\o}gaard, Anders and
Tsarfaty, Reut and
Zeman, Dan",
booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.iwpt-1.21",
doi = "10.18653/v1/2020.iwpt-1.21",
pages = "206--214",
abstract = "To accomplish the shared task on dependency parsing we explore the use of a linear transition-based neural dependency parser as well as a combination of three of them by means of a linear tree combination algorithm. We train separate models for each language on the shared task data. We compare our base parser with two biaffine parsers and also present an ensemble combination of all five parsers, which achieves an average UAS 1.88 point lower than the top official submission. For producing the enhanced dependencies, we exploit a hybrid approach, coupling an algorithmic graph transformation of the dependency tree with predictions made by a multitask machine learning model.",
}
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%0 Conference Proceedings
%T Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies
%A Attardi, Giuseppe
%A Sartiano, Daniele
%A Simi, Maria
%Y Bouma, Gosse
%Y Matsumoto, Yuji
%Y Oepen, Stephan
%Y Sagae, Kenji
%Y Seddah, Djamé
%Y Sun, Weiwei
%Y Søgaard, Anders
%Y Tsarfaty, Reut
%Y Zeman, Dan
%S Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F attardi-etal-2020-linear
%X To accomplish the shared task on dependency parsing we explore the use of a linear transition-based neural dependency parser as well as a combination of three of them by means of a linear tree combination algorithm. We train separate models for each language on the shared task data. We compare our base parser with two biaffine parsers and also present an ensemble combination of all five parsers, which achieves an average UAS 1.88 point lower than the top official submission. For producing the enhanced dependencies, we exploit a hybrid approach, coupling an algorithmic graph transformation of the dependency tree with predictions made by a multitask machine learning model.
%R 10.18653/v1/2020.iwpt-1.21
%U https://aclanthology.org/2020.iwpt-1.21
%U https://doi.org/10.18653/v1/2020.iwpt-1.21
%P 206-214
Markdown (Informal)
[Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies](https://aclanthology.org/2020.iwpt-1.21) (Attardi et al., IWPT 2020)
ACL