Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond

• URL: http://arxiv.org/abs/2010.12405v3
• Date: Tue, 22 Jun 2021 13:52:14 GMT
• Title: Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond
• Authors: Xin Li, Lidong Bing, Wenxuan Zhang, Zheng Li, Wai Lam
• Abstract summary: Cross-lingual adaptation with multilingual pre-trained language models (mPTLMs) mainly consists of two lines of works: zero-shot approach and translation-based approach. We propose a novel framework to consolidate the zero-shot approach and the translation-based approach for better adaptation performance.
• Score: 58.80417796087894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-lingual adaptation with multilingual pre-trained language models (mPTLMs) mainly consists of two lines of works: zero-shot approach and translation-based approach, which have been studied extensively on the sequence-level tasks. We further verify the efficacy of these cross-lingual adaptation approaches by evaluating their performances on more fine-grained sequence tagging tasks. After re-examining their strengths and drawbacks, we propose a novel framework to consolidate the zero-shot approach and the translation-based approach for better adaptation performance. Instead of simply augmenting the source data with the machine-translated data, we tailor-make a warm-up mechanism to quickly update the mPTLMs with the gradients estimated on a few translated data. Then, the adaptation approach is applied to the refined parameters and the cross-lingual transfer is performed in a warm-start way. The experimental results on nine target languages demonstrate that our method is beneficial to the cross-lingual adaptation of various sequence tagging tasks.

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