mlsanityData to 7 Oct 2026

Cross-Lingual Phrase Retrieval

arXiv 2204.08887 ↗First posted 19 Apr 2022cs.CLcited by 2

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Abstract (arXiv)

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/.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 34 of its 45 references that are in the map. First posted on arXiv 19 Apr 2022.

Builds on

34 of 45 references.

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22 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023On the Possibilities of AI-Generated Text Detectioncited by 88
  2. 2024Cross-lingual Contextualized Phrase Retrievalcited by 1