@inproceedings{li-etal-2020-flat,
title = "{FLAT}: {C}hinese {NER} Using Flat-Lattice Transformer",
author = "Li, Xiaonan and
Yan, Hang and
Qiu, Xipeng and
Huang, Xuanjing",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.611",
doi = "10.18653/v1/2020.acl-main.611",
pages = "6836--6842",
abstract = "Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, since the lattice structure is complex and dynamic, the lattice-based models are hard to fully utilize the parallel computation of GPUs and usually have a low inference speed. In this paper, we propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans. Each span corresponds to a character or latent word and its position in the original lattice. With the power of Transformer and well-designed position encoding, FLAT can fully leverage the lattice information and has an excellent parallel ability. Experiments on four datasets show FLAT outperforms other lexicon-based models in performance and efficiency.",
}
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<abstract>Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, since the lattice structure is complex and dynamic, the lattice-based models are hard to fully utilize the parallel computation of GPUs and usually have a low inference speed. In this paper, we propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans. Each span corresponds to a character or latent word and its position in the original lattice. With the power of Transformer and well-designed position encoding, FLAT can fully leverage the lattice information and has an excellent parallel ability. Experiments on four datasets show FLAT outperforms other lexicon-based models in performance and efficiency.</abstract>
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%0 Conference Proceedings
%T FLAT: Chinese NER Using Flat-Lattice Transformer
%A Li, Xiaonan
%A Yan, Hang
%A Qiu, Xipeng
%A Huang, Xuanjing
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-flat
%X Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, since the lattice structure is complex and dynamic, the lattice-based models are hard to fully utilize the parallel computation of GPUs and usually have a low inference speed. In this paper, we propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans. Each span corresponds to a character or latent word and its position in the original lattice. With the power of Transformer and well-designed position encoding, FLAT can fully leverage the lattice information and has an excellent parallel ability. Experiments on four datasets show FLAT outperforms other lexicon-based models in performance and efficiency.
%R 10.18653/v1/2020.acl-main.611
%U https://aclanthology.org/2020.acl-main.611
%U https://doi.org/10.18653/v1/2020.acl-main.611
%P 6836-6842
Markdown (Informal)
[FLAT: Chinese NER Using Flat-Lattice Transformer](https://aclanthology.org/2020.acl-main.611) (Li et al., ACL 2020)
ACL
- Xiaonan Li, Hang Yan, Xipeng Qiu, and Xuanjing Huang. 2020. FLAT: Chinese NER Using Flat-Lattice Transformer. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6836–6842, Online. Association for Computational Linguistics.