@inproceedings{jiang-etal-2019-regularization,
title = "A Regularization-based Framework for Bilingual Grammar Induction",
author = "Jiang, Yong and
Han, Wenjuan and
Tu, Kewei",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1148/",
doi = "10.18653/v1/D19-1148",
pages = "1423--1428",
abstract = "Grammar induction aims to discover syntactic structures from unannotated sentences. In this paper, we propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language. Unlike previous work on multilingual grammar induction, our approach does not rely on any external resource, such as parallel corpora, word alignments or linguistic phylogenetic trees. We propose three regularization methods that encourage similarity between model parameters, dependency edge scores, and parse trees respectively. We deploy our methods on a state-of-the-art unsupervised discriminative parser and evaluate it on both transfer grammar induction and bilingual grammar induction. Empirical results on multiple languages show that our methods outperform strong baselines."
}
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<abstract>Grammar induction aims to discover syntactic structures from unannotated sentences. In this paper, we propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language. Unlike previous work on multilingual grammar induction, our approach does not rely on any external resource, such as parallel corpora, word alignments or linguistic phylogenetic trees. We propose three regularization methods that encourage similarity between model parameters, dependency edge scores, and parse trees respectively. We deploy our methods on a state-of-the-art unsupervised discriminative parser and evaluate it on both transfer grammar induction and bilingual grammar induction. Empirical results on multiple languages show that our methods outperform strong baselines.</abstract>
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%0 Conference Proceedings
%T A Regularization-based Framework for Bilingual Grammar Induction
%A Jiang, Yong
%A Han, Wenjuan
%A Tu, Kewei
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F jiang-etal-2019-regularization
%X Grammar induction aims to discover syntactic structures from unannotated sentences. In this paper, we propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language. Unlike previous work on multilingual grammar induction, our approach does not rely on any external resource, such as parallel corpora, word alignments or linguistic phylogenetic trees. We propose three regularization methods that encourage similarity between model parameters, dependency edge scores, and parse trees respectively. We deploy our methods on a state-of-the-art unsupervised discriminative parser and evaluate it on both transfer grammar induction and bilingual grammar induction. Empirical results on multiple languages show that our methods outperform strong baselines.
%R 10.18653/v1/D19-1148
%U https://aclanthology.org/D19-1148/
%U https://doi.org/10.18653/v1/D19-1148
%P 1423-1428
Markdown (Informal)
[A Regularization-based Framework for Bilingual Grammar Induction](https://aclanthology.org/D19-1148/) (Jiang et al., EMNLP-IJCNLP 2019)
ACL
- Yong Jiang, Wenjuan Han, and Kewei Tu. 2019. A Regularization-based Framework for Bilingual Grammar Induction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1423–1428, Hong Kong, China. Association for Computational Linguistics.