Zero-shot Cross-lingual Transfer without Parallel Corpus

• URL: http://arxiv.org/abs/2310.04726v1
• Date: Sat, 7 Oct 2023 07:54:22 GMT
• Title: Zero-shot Cross-lingual Transfer without Parallel Corpus
• Authors: Yuyang Zhang, Xiaofeng Han, Baojun Wang
• Abstract summary: We propose a novel approach to conduct zero-shot cross-lingual transfer with a pre-trained model. It consists of a Bilingual Task Fitting module that applies task-related bilingual information alignment. A self-training module generates pseudo soft and hard labels for unlabeled data and utilizes them to conduct self-training.
• Score: 6.937772043639308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, although pre-trained language models have achieved great success on multilingual NLP (Natural Language Processing) tasks, the lack of training data on many tasks in low-resource languages still limits their performance. One effective way of solving that problem is to transfer knowledge from rich-resource languages to low-resource languages. However, many previous works on cross-lingual transfer rely heavily on the parallel corpus or translation models, which are often difficult to obtain. We propose a novel approach to conduct zero-shot cross-lingual transfer with a pre-trained model. It consists of a Bilingual Task Fitting module that applies task-related bilingual information alignment; a self-training module generates pseudo soft and hard labels for unlabeled data and utilizes them to conduct self-training. We got the new SOTA on different tasks without any dependencies on the parallel corpus or translation models.

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