@inproceedings{li-etal-2021-metats,
title = "{M}eta{TS}: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision",
author = "Li, Zheng and
Zhang, Danqing and
Cao, Tianyu and
Wei, Ying and
Song, Yiwei and
Yin, Bing",
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.255/",
doi = "10.18653/v1/2021.emnlp-main.255",
pages = "3183--3196",
abstract = "Sequence labeling aims to predict a fine-grained sequence of labels for the text. However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data. This is exacerbated when we meet a diverse range of languages. In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages. Specifically, we propose a Meta Teacher-Student (MetaTS) Network, a novel meta learning method to alleviate data scarcity by leveraging large multilingual unlabeled data. Prior teacher-student frameworks of self-training rely on rigid teaching strategies, which may hardly produce high-quality pseudo-labels for consecutive and interdependent tokens. On the contrary, MetaTS allows the teacher to dynamically adapt its pseudo-annotation strategies by the student`s feedback on the generated pseudo-labeled data of each language and thus mitigate error propagation from noisy pseudo-labels. Extensive experiments on both public and real-world multilingual sequence labeling datasets empirically demonstrate the effectiveness of MetaTS."
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%0 Conference Proceedings
%T MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision
%A Li, Zheng
%A Zhang, Danqing
%A Cao, Tianyu
%A Wei, Ying
%A Song, Yiwei
%A Yin, Bing
%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 li-etal-2021-metats
%X Sequence labeling aims to predict a fine-grained sequence of labels for the text. However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data. This is exacerbated when we meet a diverse range of languages. In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages. Specifically, we propose a Meta Teacher-Student (MetaTS) Network, a novel meta learning method to alleviate data scarcity by leveraging large multilingual unlabeled data. Prior teacher-student frameworks of self-training rely on rigid teaching strategies, which may hardly produce high-quality pseudo-labels for consecutive and interdependent tokens. On the contrary, MetaTS allows the teacher to dynamically adapt its pseudo-annotation strategies by the student‘s feedback on the generated pseudo-labeled data of each language and thus mitigate error propagation from noisy pseudo-labels. Extensive experiments on both public and real-world multilingual sequence labeling datasets empirically demonstrate the effectiveness of MetaTS.
%R 10.18653/v1/2021.emnlp-main.255
%U https://aclanthology.org/2021.emnlp-main.255/
%U https://doi.org/10.18653/v1/2021.emnlp-main.255
%P 3183-3196
Markdown (Informal)
[MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision](https://aclanthology.org/2021.emnlp-main.255/) (Li et al., EMNLP 2021)
ACL