We propose a fully unsupervised framework for ad-hoc cross-lingual
information retrieval (CLIR) which requires no bilingual data at all. The
framework leverages shared cross-lingual word embedding spaces in which terms,
queries, and documents can be represented, irrespective of their actual
language. The shared embedding spaces are induced solely on the basis of
monolingual corpora in two languages through an iterative process based on
adversarial neural networks. Our experiments on the standard CLEF CLIR
collections for three language pairs of varying degrees of language similarity
(English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed
fully unsupervised approach. Our CLIR models with unsupervised cross-lingual
embeddings outperform baselines that utilize cross-lingual embeddings induced
relying on word-level and document-level alignments. We then demonstrate that
further improvements can be achieved by unsupervised ensemble CLIR models. We
believe that the proposed framework is the first step towards development of
effective CLIR models for language pairs and domains where parallel data are
scarce or non-existent.

提出一个针对跨语言信息检索的全面无监督框架，通过基于单语语料库构建共享的跨语言词嵌入空间，利用来自对抗神经网络的迭代过程实现无需双语数据的检索，实验验证了该方法的有效性，并通过无监督集成 CLIR 模型提升性能。