Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching

• URL: http://arxiv.org/abs/2406.13361v1
• Date: Wed, 19 Jun 2024 09:06:24 GMT
• Title: Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching
• Authors: Zhuoran Li, Chunming Hu, Junfan Chen, Zhijun Chen, Xiaohui Guo, Richong Zhang,
• Abstract summary: Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. We propose a Progressive Code-Switching (PCS) method to gradually generate moderately difficult code-switching examples for the model. Experiments show our model achieves state-of-the-art results on three different zero-shot cross-lingual transfer tasks across ten languages.
• Score: 35.27850496374157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingual contextual word representations. However, uncontrolled and over-replaced code-switching would augment dirty samples to model training. In other words, the excessive code-switching text samples will negatively hurt the models' cross-lingual transferability. To this end, we propose a Progressive Code-Switching (PCS) method to gradually generate moderately difficult code-switching examples for the model to discriminate from easy to hard. The idea is to incorporate progressively the preceding learned multilingual knowledge using easier code-switching data to guide model optimization on succeeding harder code-switching data. Specifically, we first design a difficulty measurer to measure the impact of replacing each word in a sentence based on the word relevance score. Then a code-switcher generates the code-switching data of increasing difficulty via a controllable temperature variable. In addition, a training scheduler decides when to sample harder code-switching data for model training. Experiments show our model achieves state-of-the-art results on three different zero-shot cross-lingual transfer tasks across ten languages.

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