Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching
This addresses the issue of dirty data augmentation in multilingual NLP for researchers and practitioners, but it is incremental as it builds on existing code-switching techniques.
The paper tackles the problem of uncontrolled code-switching in cross-lingual transfer by proposing a Progressive Code-Switching method to generate moderately difficult examples, achieving state-of-the-art results on three zero-shot cross-lingual transfer tasks across ten languages.
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.