@inproceedings{yang-etal-2019-fine,
title = "Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation",
author = "Yang, Huiyun and
Huang, Shujian and
Dai, Xin-Yu and
Chen, Jiajun",
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/D19-1429",
doi = "10.18653/v1/D19-1429",
pages = "4197--4206",
abstract = "In sequence labeling, previous domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual target domain samples, which may lead to negative transfer results for certain samples. Besides, an important characteristic of sequence labeling tasks is that different elements within a given sample may also have diverse domain relevance, which requires further consideration. To take the multi-level domain relevance discrepancy into account, in this paper, we propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from the source domain model. Experiments on three sequence labeling tasks show that our fine-grained knowledge fusion model outperforms strong baselines and other state-of-the-art sequence labeling domain adaptation methods.",
}
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<abstract>In sequence labeling, previous domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual target domain samples, which may lead to negative transfer results for certain samples. Besides, an important characteristic of sequence labeling tasks is that different elements within a given sample may also have diverse domain relevance, which requires further consideration. To take the multi-level domain relevance discrepancy into account, in this paper, we propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from the source domain model. Experiments on three sequence labeling tasks show that our fine-grained knowledge fusion model outperforms strong baselines and other state-of-the-art sequence labeling domain adaptation methods.</abstract>
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%0 Conference Proceedings
%T Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation
%A Yang, Huiyun
%A Huang, Shujian
%A Dai, Xin-Yu
%A Chen, Jiajun
%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 yang-etal-2019-fine
%X In sequence labeling, previous domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual target domain samples, which may lead to negative transfer results for certain samples. Besides, an important characteristic of sequence labeling tasks is that different elements within a given sample may also have diverse domain relevance, which requires further consideration. To take the multi-level domain relevance discrepancy into account, in this paper, we propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from the source domain model. Experiments on three sequence labeling tasks show that our fine-grained knowledge fusion model outperforms strong baselines and other state-of-the-art sequence labeling domain adaptation methods.
%R 10.18653/v1/D19-1429
%U https://aclanthology.org/D19-1429
%U https://doi.org/10.18653/v1/D19-1429
%P 4197-4206
Markdown (Informal)
[Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation](https://aclanthology.org/D19-1429) (Yang et al., EMNLP-IJCNLP 2019)
ACL
- Huiyun Yang, Shujian Huang, Xin-Yu Dai, and Jiajun Chen. 2019. Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation. 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 4197–4206, Hong Kong, China. Association for Computational Linguistics.