Multilingual End-to-end Dependency Parsing with Linguistic Typology knowledge

Chinmay Choudhary, Colm O’riordan


Abstract
We evaluate a Multilingual End-to-end BERT based Dependency Parser which parses an input sentence by directly predicting the relative head-position for each word within it. Our model is a Cross-lingual dependency parser which is trained on a diverse polyglot corpus of high-resource source languages, and is applied on a low-resource target language. To make model more robust to typological variations between source and target languages, and to facilitate the cross-lingual transferring, we utilized the Linguistic typology knowledge, available in typological databases WALS and URIEL. We induce such typology knowledge within our model through an auxiliary task within Multi-task Learning framework.
Anthology ID:
2023.sigtyp-1.2
Volume:
Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Lisa Beinborn, Koustava Goswami, Saliha Muradoğlu, Alexey Sorokin, Ritesh Kumar, Andreas Shcherbakov, Edoardo M. Ponti, Ryan Cotterell, Ekaterina Vylomova
Venue:
SIGTYP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–21
Language:
URL:
https://aclanthology.org/2023.sigtyp-1.2
DOI:
10.18653/v1/2023.sigtyp-1.2
Bibkey:
Cite (ACL):
Chinmay Choudhary and Colm O’riordan. 2023. Multilingual End-to-end Dependency Parsing with Linguistic Typology knowledge. In Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 12–21, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Multilingual End-to-end Dependency Parsing with Linguistic Typology knowledge (Choudhary & O’riordan, SIGTYP 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2023.sigtyp-1.2.pdf