  Recently multi-lingual pre-trained language models (PLM) such as mBERT and
XLM-R have achieved impressive strides in cross-lingual dense retrieval.
Despite its successes, they are general-purpose PLM while the multilingual PLM
tailored for cross-lingual retrieval is still unexplored. Motivated by an
observation that the sentences in parallel documents are approximately in the
same order, which is universal across languages, we propose to model this
sequential sentence relation to facilitate cross-lingual representation
learning. Specifically, we propose a multilingual PLM called masked sentence
model (MSM), which consists of a sentence encoder to generate the sentence
representations, and a document encoder applied to a sequence of sentence
vectors from a document. The document encoder is shared for all languages to
model the universal sequential sentence relation across languages. To train the
model, we propose a masked sentence prediction task, which masks and predicts
the sentence vector via a hierarchical contrastive loss with sampled negatives.
Comprehensive experiments on four cross-lingual retrieval tasks show MSM
significantly outperforms existing advanced pre-training models, demonstrating
the effectiveness and stronger cross-lingual retrieval capabilities of our
approach. Code and model will be available.
