@inproceedings{choudhary-oriordan-2023-multilingual,
title = "Multilingual End-to-end Dependency Parsing with Linguistic Typology knowledge",
author = "Choudhary, Chinmay and
O{'}riordan, Colm",
editor = "Beinborn, Lisa and
Goswami, Koustava and
Murado{\u{g}}lu, Saliha and
Sorokin, Alexey and
Kumar, Ritesh and
Shcherbakov, Andreas and
Ponti, Edoardo M. and
Cotterell, Ryan and
Vylomova, Ekaterina",
booktitle = "Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigtyp-1.2",
doi = "10.18653/v1/2023.sigtyp-1.2",
pages = "12--21",
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.",
}
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%0 Conference Proceedings
%T Multilingual End-to-end Dependency Parsing with Linguistic Typology knowledge
%A Choudhary, Chinmay
%A O’riordan, Colm
%Y Beinborn, Lisa
%Y Goswami, Koustava
%Y Muradoğlu, Saliha
%Y Sorokin, Alexey
%Y Kumar, Ritesh
%Y Shcherbakov, Andreas
%Y Ponti, Edoardo M.
%Y Cotterell, Ryan
%Y Vylomova, Ekaterina
%S Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F choudhary-oriordan-2023-multilingual
%X 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.
%R 10.18653/v1/2023.sigtyp-1.2
%U https://aclanthology.org/2023.sigtyp-1.2
%U https://doi.org/10.18653/v1/2023.sigtyp-1.2
%P 12-21
Markdown (Informal)
[Multilingual End-to-end Dependency Parsing with Linguistic Typology knowledge](https://aclanthology.org/2023.sigtyp-1.2) (Choudhary & O’riordan, SIGTYP 2023)
ACL