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    <abstract>Cross-lingual named entity recognition (NER) suffers from data scarcity in the target languages, especially under zero-shot settings. Existing translate-train or knowledge distillation methods attempt to bridge the language gap, but often introduce a high level of noise. To solve this problem, consistency training methods regularize the model to be robust towards perturbations on data or hidden states. However, such methods are likely to violate the consistency hypothesis, or mainly focus on coarse-grain consistency. We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropout-based consistency training on labeled source-language data. ConNER effectively leverages unlabeled target-language data and alleviates overfitting on the source language to enhance the cross-lingual adaptability. Experimental results show our ConNER achieves consistent improvement over various baseline methods.</abstract>
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%0 Conference Proceedings
%T ConNER: Consistency Training for Cross-lingual Named Entity Recognition
%A Zhou, Ran
%A Li, Xin
%A Bing, Lidong
%A Cambria, Erik
%A Si, Luo
%A Miao, Chunyan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhou-etal-2022-conner
%X Cross-lingual named entity recognition (NER) suffers from data scarcity in the target languages, especially under zero-shot settings. Existing translate-train or knowledge distillation methods attempt to bridge the language gap, but often introduce a high level of noise. To solve this problem, consistency training methods regularize the model to be robust towards perturbations on data or hidden states. However, such methods are likely to violate the consistency hypothesis, or mainly focus on coarse-grain consistency. We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropout-based consistency training on labeled source-language data. ConNER effectively leverages unlabeled target-language data and alleviates overfitting on the source language to enhance the cross-lingual adaptability. Experimental results show our ConNER achieves consistent improvement over various baseline methods.
%R 10.18653/v1/2022.emnlp-main.577
%U https://aclanthology.org/2022.emnlp-main.577
%U https://doi.org/10.18653/v1/2022.emnlp-main.577
%P 8438-8449

Markdown (Informal)

[ConNER: Consistency Training for Cross-lingual Named Entity Recognition](https://aclanthology.org/2022.emnlp-main.577) (Zhou et al., EMNLP 2022)

• ConNER: Consistency Training for Cross-lingual Named Entity Recognition (Zhou et al., EMNLP 2022)

ACL

• Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, and Chunyan Miao. 2022. ConNER: Consistency Training for Cross-lingual Named Entity Recognition. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8438–8449, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.