@inproceedings{rasooli-collins-2019-low,
title = "Low-Resource Syntactic Transfer with Unsupervised Source Reordering",
author = "Rasooli, Mohammad Sadegh and
Collins, Michael",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1385/",
doi = "10.18653/v1/N19-1385",
pages = "3845--3856",
abstract = "We describe a cross-lingual transfer method for dependency parsing that takes into account the problem of word order differences between source and target languages. Our model only relies on the Bible, a considerably smaller parallel data than the commonly used parallel data in transfer methods. We use the concatenation of projected trees from the Bible corpus, and the gold-standard treebanks in multiple source languages along with cross-lingual word representations. We demonstrate that reordering the source treebanks before training on them for a target language improves the accuracy of languages outside the European language family. Our experiments on 68 treebanks (38 languages) in the Universal Dependencies corpus achieve a high accuracy for all languages. Among them, our experiments on 16 treebanks of 12 non-European languages achieve an average UAS absolute improvement of 3.3{\%} over a state-of-the-art method."
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%0 Conference Proceedings
%T Low-Resource Syntactic Transfer with Unsupervised Source Reordering
%A Rasooli, Mohammad Sadegh
%A Collins, Michael
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F rasooli-collins-2019-low
%X We describe a cross-lingual transfer method for dependency parsing that takes into account the problem of word order differences between source and target languages. Our model only relies on the Bible, a considerably smaller parallel data than the commonly used parallel data in transfer methods. We use the concatenation of projected trees from the Bible corpus, and the gold-standard treebanks in multiple source languages along with cross-lingual word representations. We demonstrate that reordering the source treebanks before training on them for a target language improves the accuracy of languages outside the European language family. Our experiments on 68 treebanks (38 languages) in the Universal Dependencies corpus achieve a high accuracy for all languages. Among them, our experiments on 16 treebanks of 12 non-European languages achieve an average UAS absolute improvement of 3.3% over a state-of-the-art method.
%R 10.18653/v1/N19-1385
%U https://aclanthology.org/N19-1385/
%U https://doi.org/10.18653/v1/N19-1385
%P 3845-3856
Markdown (Informal)
[Low-Resource Syntactic Transfer with Unsupervised Source Reordering](https://aclanthology.org/N19-1385/) (Rasooli & Collins, NAACL 2019)
ACL
- Mohammad Sadegh Rasooli and Michael Collins. 2019. Low-Resource Syntactic Transfer with Unsupervised Source Reordering. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3845–3856, Minneapolis, Minnesota. Association for Computational Linguistics.