@inproceedings{maimaiti-etal-2021-segment,
title = "Segment, Mask, and Predict: Augmenting {C}hinese Word Segmentation with Self-Supervision",
author = "Maimaiti, Mieradilijiang and
Liu, Yang and
Zheng, Yuanhang and
Chen, Gang and
Huang, Kaiyu and
Zhang, Ji and
Luan, Huanbo and
Sun, Maosong",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.158",
doi = "10.18653/v1/2021.emnlp-main.158",
pages = "2068--2077",
abstract = "Recent state-of-the-art (SOTA) effective neural network methods and fine-tuning methods based on pre-trained models (PTM) have been used in Chinese word segmentation (CWS), and they achieve great results. However, previous works focus on training the models with the fixed corpus at every iteration. The intermediate generated information is also valuable. Besides, the robustness of the previous neural methods is limited by the large-scale annotated data. There are a few noises in the annotated corpus. Limited efforts have been made by previous studies to deal with such problems. In this work, we propose a self-supervised CWS approach with a straightforward and effective architecture. First, we train a word segmentation model and use it to generate the segmentation results. Then, we use a revised masked language model (MLM) to evaluate the quality of the segmentation results based on the predictions of the MLM. Finally, we leverage the evaluations to aid the training of the segmenter by improved minimum risk training. Experimental results show that our approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness.",
}
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<abstract>Recent state-of-the-art (SOTA) effective neural network methods and fine-tuning methods based on pre-trained models (PTM) have been used in Chinese word segmentation (CWS), and they achieve great results. However, previous works focus on training the models with the fixed corpus at every iteration. The intermediate generated information is also valuable. Besides, the robustness of the previous neural methods is limited by the large-scale annotated data. There are a few noises in the annotated corpus. Limited efforts have been made by previous studies to deal with such problems. In this work, we propose a self-supervised CWS approach with a straightforward and effective architecture. First, we train a word segmentation model and use it to generate the segmentation results. Then, we use a revised masked language model (MLM) to evaluate the quality of the segmentation results based on the predictions of the MLM. Finally, we leverage the evaluations to aid the training of the segmenter by improved minimum risk training. Experimental results show that our approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness.</abstract>
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%0 Conference Proceedings
%T Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision
%A Maimaiti, Mieradilijiang
%A Liu, Yang
%A Zheng, Yuanhang
%A Chen, Gang
%A Huang, Kaiyu
%A Zhang, Ji
%A Luan, Huanbo
%A Sun, Maosong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F maimaiti-etal-2021-segment
%X Recent state-of-the-art (SOTA) effective neural network methods and fine-tuning methods based on pre-trained models (PTM) have been used in Chinese word segmentation (CWS), and they achieve great results. However, previous works focus on training the models with the fixed corpus at every iteration. The intermediate generated information is also valuable. Besides, the robustness of the previous neural methods is limited by the large-scale annotated data. There are a few noises in the annotated corpus. Limited efforts have been made by previous studies to deal with such problems. In this work, we propose a self-supervised CWS approach with a straightforward and effective architecture. First, we train a word segmentation model and use it to generate the segmentation results. Then, we use a revised masked language model (MLM) to evaluate the quality of the segmentation results based on the predictions of the MLM. Finally, we leverage the evaluations to aid the training of the segmenter by improved minimum risk training. Experimental results show that our approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness.
%R 10.18653/v1/2021.emnlp-main.158
%U https://aclanthology.org/2021.emnlp-main.158
%U https://doi.org/10.18653/v1/2021.emnlp-main.158
%P 2068-2077
Markdown (Informal)
[Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision](https://aclanthology.org/2021.emnlp-main.158) (Maimaiti et al., EMNLP 2021)
ACL
- Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, and Maosong Sun. 2021. Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2068–2077, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.