TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition

Yichun Zhao, Jintao Du, Gongshen Liu, Huijia Zhu


Abstract
Zero-Resource Cross-Lingual Named Entity Recognition aims at training an NER model of the target language using only labeled source language data and unlabeled target language data. Existing methods are mainly divided into three categories: model transfer based, data transfer based and knowledge transfer based. Each method has its own disadvantages, and combining more than one of them often leads to better performance. However, the performance of data transfer based methods is often limited by inevitable noise in the translation process. To handle the problem, we propose a framework named TransAdv to mitigate lexical and syntactic errors of word-by-word translated data, better utilizing the data by multi-level adversarial learning and multi-model knowledge distillation. Extensive experiments are conducted over 6 target languages with English as the source language, and the results show that TransAdv achieves competitive performance to the state-of-the-art models.
Anthology ID:
2022.findings-emnlp.52
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
742–749
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.52
DOI:
10.18653/v1/2022.findings-emnlp.52
Bibkey:
Cite (ACL):
Yichun Zhao, Jintao Du, Gongshen Liu, and Huijia Zhu. 2022. TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 742–749, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition (Zhao et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.52.pdf