  Cross-lingual retrieval aims to retrieve relevant text across languages.
Current methods typically achieve cross-lingual retrieval by learning
language-agnostic text representations in word or sentence level. However, how
to learn phrase representations for cross-lingual phrase retrieval is still an
open problem. In this paper, we propose XPR, a cross-lingual phrase retriever
that extracts phrase representations from unlabeled example sentences.
Moreover, we create a large-scale cross-lingual phrase retrieval dataset, which
contains 65K bilingual phrase pairs and 4.2M example sentences in 8
English-centric language pairs. Experimental results show that XPR outperforms
state-of-the-art baselines which utilize word-level or sentence-level
representations. XPR also shows impressive zero-shot transferability that
enables the model to perform retrieval in an unseen language pair during
training. Our dataset, code, and trained models are publicly available at
www.github.com/cwszz/XPR/.
