@inproceedings{li-etal-2019-semi-supervised-domain,
title = "Semi-supervised Domain Adaptation for Dependency Parsing",
author = "Li, Zhenghua and
Peng, Xue and
Zhang, Min and
Wang, Rui and
Si, Luo",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1229",
doi = "10.18653/v1/P19-1229",
pages = "2386--2395",
abstract = "During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data. However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing. This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-aware datasets. We propose a simple domain embedding approach to merge the source- and target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning. In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margin.",
}
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<abstract>During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data. However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing. This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-aware datasets. We propose a simple domain embedding approach to merge the source- and target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning. In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margin.</abstract>
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%0 Conference Proceedings
%T Semi-supervised Domain Adaptation for Dependency Parsing
%A Li, Zhenghua
%A Peng, Xue
%A Zhang, Min
%A Wang, Rui
%A Si, Luo
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F li-etal-2019-semi-supervised-domain
%X During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data. However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing. This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-aware datasets. We propose a simple domain embedding approach to merge the source- and target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning. In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margin.
%R 10.18653/v1/P19-1229
%U https://aclanthology.org/P19-1229
%U https://doi.org/10.18653/v1/P19-1229
%P 2386-2395
Markdown (Informal)
[Semi-supervised Domain Adaptation for Dependency Parsing](https://aclanthology.org/P19-1229) (Li et al., ACL 2019)
ACL
- Zhenghua Li, Xue Peng, Min Zhang, Rui Wang, and Luo Si. 2019. Semi-supervised Domain Adaptation for Dependency Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2386–2395, Florence, Italy. Association for Computational Linguistics.