End-to-end mBERT based Seq2seq Enhanced Dependency Parser with Linguistic Typology knowledge

Chinmay Choudhary, Colm O’riordan


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.
Anthology ID:
2021.iwpt-1.24
Volume:
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | IWPT
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
225–232
Language:
URL:
https://aclanthology.org/2021.iwpt-1.24
DOI:
10.18653/v1/2021.iwpt-1.24
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.iwpt-1.24.pdf