Alternating Language Modeling for Cross-Lingual Pre-Training

Language model pre-training has achieved success in many natural language processing tasks. Existing methods for cross-lingual pre-training adopt Translation Language Model to predict masked words with the concatenation of the source sentence and its target equivalent. In this work, we introduce a novel cross-lingual pre-training method, called Alternating Language Modeling (ALM). It code-switches sentences of different languages rather than simple concatenation, hoping to capture the rich cross-lingual context of words and phrases. More specifically, we randomly substitute source phrases with target translations to create code-switched sentences. Then, we use these code-switched data to train ALM model to learn to predict words of different languages. We evaluate our pre-training ALM on the downstream tasks of machine translation and cross-lingual classification. Experiments show that ALM can outperform the previous pretraining methods on three benchmarks. 1

Authors

• Jian Yang
• Shuming Ma
• Dongdong Zhang
• ShuangZhi Wu
• Zhoujun Li CCSE, Beihang University, China
• Ming Zhou Langboat Technology, Beijing, China

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