mlsanityData to 7 Oct 2026

Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval

arXiv 2304.01019 ↗First posted 3 Apr 2023cs.IR cs.CLcited by 4

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Abstract (arXiv)

The advent of multilingual language models has generated a resurgence of interest in cross-lingual information retrieval (CLIR), which is the task of searching documents in one language with queries from another. However, the rapid pace of progress has led to a confusing panoply of methods and reproducibility has lagged behind the state of the art. In this context, our work makes two important contributions: First, we provide a conceptual framework for organizing different approaches to cross-lingual retrieval using multi-stage architectures for mono-lingual retrieval as a scaffold. Second, we implement simple yet effective reproducible baselines in the Anserini and Pyserini IR toolkits for test collections from the TREC 2022 NeuCLIR Track, in Persian, Russian, and Chinese. Our efforts are built on a collaboration of the two teams that submitted the most effective runs to the TREC evaluation. These contributions provide a firm foundation for future advances.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 3 Apr 2023.

Builds on

32 of 53 references.

References · 32

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20 more
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2024JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resourcescited by 15
  2. 2023Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languagescited by 8
  3. 2023IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languagescited by 5
  4. 2025CLIRudit: Cross-Lingual Information Retrieval of Scientific Documentscited by 2