@inproceedings{choudhary-oriordan-2021-end,
title = "End-to-end m{BERT} based Seq2seq Enhanced Dependency Parser with Linguistic Typology knowledge",
author = "Choudhary, Chinmay and
O{'}riordan, Colm",
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-1.24",
doi = "10.18653/v1/2021.iwpt-1.24",
pages = "225--232",
abstract = "We describe the NUIG solution for IWPT 2021 Shared Task of Enhanced Dependency (ED) parsing in multiple languages. For this shared task, we propose and evaluate an End-to-end Seq2seq mBERT-based ED parser which predicts the ED-parse tree of a given input sentence as a relative head-position tag-sequence. Our proposed model is a multitasking neural-network which performs five key tasks simultaneously namely UPOS tagging, UFeat tagging, Lemmatization, Dependency-parsing and ED-parsing. Furthermore we utilise the linguistic typology available in the WALS database to improve the ability of our proposed end-to-end parser to transfer across languages. Results show that our proposed Seq2seq ED-parser performs on par with state-of-the-art ED-parser despite having a much simpler de- sign.",
}
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%0 Conference Proceedings
%T End-to-end mBERT based Seq2seq Enhanced Dependency Parser with Linguistic Typology knowledge
%A Choudhary, Chinmay
%A O’riordan, Colm
%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 choudhary-oriordan-2021-end
%X We describe the NUIG solution for IWPT 2021 Shared Task of Enhanced Dependency (ED) parsing in multiple languages. For this shared task, we propose and evaluate an End-to-end Seq2seq mBERT-based ED parser which predicts the ED-parse tree of a given input sentence as a relative head-position tag-sequence. Our proposed model is a multitasking neural-network which performs five key tasks simultaneously namely UPOS tagging, UFeat tagging, Lemmatization, Dependency-parsing and ED-parsing. Furthermore we utilise the linguistic typology available in the WALS database to improve the ability of our proposed end-to-end parser to transfer across languages. Results show that our proposed Seq2seq ED-parser performs on par with state-of-the-art ED-parser despite having a much simpler de- sign.
%R 10.18653/v1/2021.iwpt-1.24
%U https://aclanthology.org/2021.iwpt-1.24
%U https://doi.org/10.18653/v1/2021.iwpt-1.24
%P 225-232
Markdown (Informal)
[End-to-end mBERT based Seq2seq Enhanced Dependency Parser with Linguistic Typology knowledge](https://aclanthology.org/2021.iwpt-1.24) (Choudhary & O’riordan, IWPT 2021)
ACL