@inproceedings{grunewald-etal-2021-robertnlp,
title = "{R}obert{NLP} at the {IWPT} 2021 Shared Task: Simple Enhanced {UD} Parsing for 17 Languages",
author = {Gr{\"u}newald, Stefan and
Oertel, Frederik Tobias and
Friedrich, Annemarie},
editor = "Oepen, Stephan and
Sagae, Kenji and
Tsarfaty, Reut and
Bouma, Gosse and
Seddah, Djam{\'e} and
Zeman, Daniel",
booktitle = "Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.iwpt-1.21",
doi = "10.18653/v1/2021.iwpt-1.21",
pages = "196--203",
abstract = "This paper presents our multilingual dependency parsing system as used in the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. Our system consists of an unfactorized biaffine classifier that operates directly on fine-tuned XLM-R embeddings and generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens. To avoid sparsity issues resulting from lexicalized dependency labels, we replace lexical items in relations with placeholders at training and prediction time, later retrieving them from the parse via a hybrid rule-based/machine-learning system. In addition, we utilize model ensembling at prediction time. Our system achieves high parsing accuracy on the blind test data, ranking 3rd out of 9 with an average ELAS F1 score of 86.97.",
}
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%0 Conference Proceedings
%T RobertNLP at the IWPT 2021 Shared Task: Simple Enhanced UD Parsing for 17 Languages
%A Grünewald, Stefan
%A Oertel, Frederik Tobias
%A Friedrich, Annemarie
%Y Oepen, Stephan
%Y Sagae, Kenji
%Y Tsarfaty, Reut
%Y Bouma, Gosse
%Y Seddah, Djamé
%Y Zeman, Daniel
%S Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F grunewald-etal-2021-robertnlp
%X This paper presents our multilingual dependency parsing system as used in the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. Our system consists of an unfactorized biaffine classifier that operates directly on fine-tuned XLM-R embeddings and generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens. To avoid sparsity issues resulting from lexicalized dependency labels, we replace lexical items in relations with placeholders at training and prediction time, later retrieving them from the parse via a hybrid rule-based/machine-learning system. In addition, we utilize model ensembling at prediction time. Our system achieves high parsing accuracy on the blind test data, ranking 3rd out of 9 with an average ELAS F1 score of 86.97.
%R 10.18653/v1/2021.iwpt-1.21
%U https://aclanthology.org/2021.iwpt-1.21
%U https://doi.org/10.18653/v1/2021.iwpt-1.21
%P 196-203
Markdown (Informal)
[RobertNLP at the IWPT 2021 Shared Task: Simple Enhanced UD Parsing for 17 Languages](https://aclanthology.org/2021.iwpt-1.21) (Grünewald et al., IWPT 2021)
ACL