Cross-Lingual Natural Language Generation via Pre-Training

In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages. We propose to pretrain the encoder and the decoder of a sequence-to-sequence model under both monolingual and cross-lingual settings. The pre-training objective encourages the model to represent different languages in the shared space, so that we can conduct zero-shot cross-lingual transfer. After the pre-training procedure, we use monolingual data to fine-tune the pre-trained model on downstream NLG tasks. Then the sequence-to-sequence model trained in a single language can be directly evaluated beyond that language (i. e. , accepting multi-lingual input and producing multi-lingual output). Experimental results on question generation and abstractive summarization show that our model outperforms the machine-translation-based pipeline methods for zero-shot cross-lingual generation. Moreover, crosslingual transfer improves NLG performance of low-resource languages by leveraging rich-resource language data. Our implementation and data are available at https: //github. com/ CZWin32768/xnlg.

Authors

• Zewen Chi
• Li Dong
• Furu Wei Microsoft
• Wenhui Wang
• Xian-Ling Mao Beijing Institute of Technology
• Heyan Huang Beijing Institute of Technology

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