@inproceedings{wei-etal-2021-masked,
title = "Masked Conditional Random Fields for Sequence Labeling",
author = "Wei, Tianwen and
Qi, Jianwei and
He, Shenghuan and
Sun, Songtao",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.163",
doi = "10.18653/v1/2021.naacl-main.163",
pages = "2024--2035",
abstract = "Conditional Random Field (CRF) based neural models are among the most performant methods for solving sequence labeling problems. Despite its great success, CRF has the shortcoming of occasionally generating illegal sequences of tags, e.g. sequences containing an {``}I-{''} tag immediately after an {``}O{''} tag, which is forbidden by the underlying BIO tagging scheme. In this work, we propose Masked Conditional Random Field (MCRF), an easy to implement variant of CRF that impose restrictions on candidate paths during both training and decoding phases. We show that the proposed method thoroughly resolves this issue and brings significant improvement over existing CRF-based models with near zero additional cost.",
}
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<abstract>Conditional Random Field (CRF) based neural models are among the most performant methods for solving sequence labeling problems. Despite its great success, CRF has the shortcoming of occasionally generating illegal sequences of tags, e.g. sequences containing an “I-” tag immediately after an “O” tag, which is forbidden by the underlying BIO tagging scheme. In this work, we propose Masked Conditional Random Field (MCRF), an easy to implement variant of CRF that impose restrictions on candidate paths during both training and decoding phases. We show that the proposed method thoroughly resolves this issue and brings significant improvement over existing CRF-based models with near zero additional cost.</abstract>
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%0 Conference Proceedings
%T Masked Conditional Random Fields for Sequence Labeling
%A Wei, Tianwen
%A Qi, Jianwei
%A He, Shenghuan
%A Sun, Songtao
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F wei-etal-2021-masked
%X Conditional Random Field (CRF) based neural models are among the most performant methods for solving sequence labeling problems. Despite its great success, CRF has the shortcoming of occasionally generating illegal sequences of tags, e.g. sequences containing an “I-” tag immediately after an “O” tag, which is forbidden by the underlying BIO tagging scheme. In this work, we propose Masked Conditional Random Field (MCRF), an easy to implement variant of CRF that impose restrictions on candidate paths during both training and decoding phases. We show that the proposed method thoroughly resolves this issue and brings significant improvement over existing CRF-based models with near zero additional cost.
%R 10.18653/v1/2021.naacl-main.163
%U https://aclanthology.org/2021.naacl-main.163
%U https://doi.org/10.18653/v1/2021.naacl-main.163
%P 2024-2035
Markdown (Informal)
[Masked Conditional Random Fields for Sequence Labeling](https://aclanthology.org/2021.naacl-main.163) (Wei et al., NAACL 2021)
ACL
- Tianwen Wei, Jianwei Qi, Shenghuan He, and Songtao Sun. 2021. Masked Conditional Random Fields for Sequence Labeling. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2024–2035, Online. Association for Computational Linguistics.