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Zero-shot cross-lingual transfer is when a multilingual model is trained toperform a task in one language and then is applied to another language.Although the zero-shot cross-lingual transfer approach has achieved success invarious classification tasks, its performance on natural language generationtasks falls short in quality and sometimes outputs an incorrect language. Inour study, we show that the fine-tuning process learns language invariantrepresentations, which is beneficial for classification tasks but harmful forgeneration tasks. Motivated by this, we propose a simple method to regularizethe model from learning language invariant representations and a method toselect model checkpoints without a development set in the target language, bothresulting in better generation quality. Experiments on three semanticallydiverse generation tasks show that our method reduces the accidentaltranslation problem by 68% and improves the ROUGE-L score by 1.5 on average.

Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution