  The cross-lingual language models are typically pretrained with masked
language modeling on multilingual text or parallel sentences. In this paper, we
introduce denoising word alignment as a new cross-lingual pre-training task.
Specifically, the model first self-labels word alignments for parallel
sentences. Then we randomly mask tokens in a bitext pair. Given a masked token,
the model uses a pointer network to predict the aligned token in the other
language. We alternately perform the above two steps in an
expectation-maximization manner. Experimental results show that our method
improves cross-lingual transferability on various datasets, especially on the
token-level tasks, such as question answering, and structured prediction.
Moreover, the model can serve as a pretrained word aligner, which achieves
reasonably low error rates on the alignment benchmarks. The code and pretrained
parameters are available at https://github.com/CZWin32768/XLM-Align.
