Abstract: 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, cross-lingual 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.

Introduction: Learning natural language generation (NLG) models heavily relies on annotated training data. However, most available datasets are collected in a single language (typically English), which restricts deploying the applications to other languages. In this work, we aim at transferring the supervision of a monolingual NLG dataset to unseen languages, so that we can boost performance for the low-resource settings. Various methods have been proposed over the years to learn universal cross-lingual word embeddings BIBREF0, BIBREF1, BIBREF2 or sentence encoders BIBREF3, BIBREF4, BIBREF5, which tries to encode multilingual texts into a single shared vector space. Despite achieving promising results on cross-lingual classification problems, cross-lingual pre-trained models purposed for NLG tasks remains relatively understudied. The cross-lingual generation problem is challenging due to the following reasons. First, it requires the models to understand multilingual input texts, and generate multilingual target sequences. So both encoder and decoder should be pre-trained together. Second, the many-to-many nature of cross-lingual NLG increases language pairs with the square of the number of languages. Third, the prediction space of cross-lingual NLG is much larger than classification tasks, which makes the knowledge transfer of decoders quite critical. Previous work mainly relies on machine translation (MT) systems to map texts to different languages. The first strand of research directly uses MT in a pipeline manner BIBREF6. For example, the input written in other languages is first translated to English, and fed into the NLG model that is trained by English data. Then the generated English text is translated back to the target language. Another strand of work employs MT to generate pseudo training data for other language pairs that are lack of annotations BIBREF7, BIBREF8. However, such methods have to use multiple MT systems, which renders them suffering from error propagation. Moreover, because the pipeline-based methods do not explicitly share the same parameter space across the languages, we can not directly transfer the task-specific supervision to other low-resource languages. In this paper, we propose a cross-lingual pre-trained model (named as Xnlg) in order to transfer monolingual NLG supervision to other pre-trained languages by fine-tuning. Specifically, Xnlg shares the same sequence-to-sequence model across languages, and is pre-trained with both monolingual and cross-lingual objectives. The model not only learns to understand multilingual input, but also is able to generate specific languages by conditioning on the encoded semantics. Figure FIGREF2 demonstrates how to use Xnlg to perform cross-lingual transfer for downstream tasks. The proposed model enables us to fine-tune the pre-trained model on monolingual NLG training data, and then evaluate it beyond a single language, including zero-shot cross-lingual generation. Besides, we explore several fine-tuning strategies to make a compromise between cross-lingual ability and task ability. In addition, we introduce two cross-lingual NLG datasets (i.e., question generation, and abstractive summarization) for evaluation, which includes three languages, namely English, Chinese, and French. Experimental results on the NLG tasks show that Xnlg achieves competitive performance compared with the machine-translation-based pipeline model in zero-shot cross-lingual settings.

Question: What is the architecture of the encoder?
Answer: 10 transformer layers, each layer containing 1024 hidden units, 8 attentions heads, and GELU activations.