%0 Conference Proceedings %T Cross-Lingual Phrase Retrieval %A Zheng, Heqi %A Zhang, Xiao %A Chi, Zewen %A Huang, Heyan %A Tan, Yan %A Lan, Tian %A Wei, Wei %A Mao, Xian-Ling %Y Muresan, Smaranda %Y Nakov, Preslav %Y Villavicencio, Aline %S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2022 %8 May %I Association for Computational Linguistics %C Dublin, Ireland %F zheng-etal-2022-cross-lingual %X 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 , 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 outperforms state-of-the-art baselines which utilize word-level or sentence-level representations. 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 github.com/cwszz/XPR/. %R 10.18653/v1/2022.acl-long.288 %U https://aclanthology.org/2022.acl-long.288 %U https://doi.org/10.18653/v1/2022.acl-long.288 %P 4193-4204