Cross-Lingual Phrase Retrieval

• URL: http://arxiv.org/abs/2204.08887v1
• Date: Tue, 19 Apr 2022 13:35:50 GMT
• Title: Cross-Lingual Phrase Retrieval
• Authors: Heqi Zheng, Xiao Zhang, Zewen Chi, Heyan Huang, Tan Yan, Tian Lan, Wei Wei, Xian-Ling Mao
• Abstract summary: 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. We propose XPR, a cross-lingual phrase retriever that extracts phrase representations from unlabeled example sentences.
• Score: 49.919180978902915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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