Cross-lingual document search is an information retrieval task in which the
queries' language differs from the documents' language. In this paper, we study
the instability of neural document search models and propose a novel end-to-end
robust framework that achieves improved performance in cross-lingual search
with different documents' languages. This framework includes a novel measure of
the relevance, smooth cosine similarity, between queries and documents, and a
novel loss function, Smooth Ordinal Search Loss, as the objective. We further
provide theoretical guarantee on the generalization error bound for the
proposed framework. We conduct experiments to compare our approach with other
document search models, and observe significant gains under commonly used
ranking metrics on the cross-lingual document retrieval task in a variety of
languages.

本文提出了一种新的端到端鲁棒框架，该框架采用新的相关性度量方法（平滑余弦相似度）和新的损失函数（平滑序列搜索损失）来解决神经文档搜索模型的不稳定性问题，并在跨语言文档检索任务中取得了显着的性能提升。