@inproceedings{meftah-etal-2019-joint,
title = "Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging",
author = "Meftah, Sara and
Tamaazousti, Youssef and
Semmar, Nasredine and
Essafi, Hassane and
Sadat, Fatiha",
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/N19-1416",
doi = "10.18653/v1/N19-1416",
pages = "4107--4112",
abstract = "Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon target-specific patterns. In this paper, we propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining the valuable source knowledge. Our experiments on POS tagging of social media texts (Tweets domain) demonstrate that our method achieves state-of-the-art performances on 3 commonly used datasets.",
}
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<abstract>Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon target-specific patterns. In this paper, we propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining the valuable source knowledge. Our experiments on POS tagging of social media texts (Tweets domain) demonstrate that our method achieves state-of-the-art performances on 3 commonly used datasets.</abstract>
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%0 Conference Proceedings
%T Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
%A Meftah, Sara
%A Tamaazousti, Youssef
%A Semmar, Nasredine
%A Essafi, Hassane
%A Sadat, Fatiha
%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 meftah-etal-2019-joint
%X Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon target-specific patterns. In this paper, we propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining the valuable source knowledge. Our experiments on POS tagging of social media texts (Tweets domain) demonstrate that our method achieves state-of-the-art performances on 3 commonly used datasets.
%R 10.18653/v1/N19-1416
%U https://aclanthology.org/N19-1416
%U https://doi.org/10.18653/v1/N19-1416
%P 4107-4112
Markdown (Informal)
[Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging](https://aclanthology.org/N19-1416) (Meftah et al., NAACL 2019)
ACL