# Overview of the TREC 2022 NeuCLIR Track

arXiv: https://arxiv.org/abs/2304.12367 · HTML: https://mlsanity.com/paper/2304.12367/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2023.

## Built on by (most cited first, 11 of 11)

- [Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval](https://mlsanity.com/paper/2311.05800/) (2023)
- [Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation](https://mlsanity.com/paper/2401.04810/) (2024)
- [MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval](https://mlsanity.com/paper/2410.11619/) (2024)
- [FaMTEB: Massive Text Embedding Benchmark in Persian Language](https://mlsanity.com/paper/2502.11571/) (2025)
- [mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval](https://mlsanity.com/paper/2501.19264/) (2025)
- [PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval](https://mlsanity.com/paper/2405.00975/) (2024)
- [Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples](https://mlsanity.com/paper/2502.08638/) (2025)
- [CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents](https://mlsanity.com/paper/2504.16264/) (2025)
- [Extending Translate-Train for ColBERT-X to African Language CLIR](https://mlsanity.com/paper/2404.08134/) (2024)
- [MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries](https://mlsanity.com/paper/2505.16631/) (2025)
- [MIMO: Multilingual Information Retrieval via Monolingual Objectives](https://mlsanity.com/paper/2605.31171/) (2026)

## Abstract (arXiv)

This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval. The main task in this year's track was ad hoc ranked retrieval of Chinese, Persian, or Russian newswire documents using queries expressed in English. Topics were developed using standard TREC processes, except that topics developed by an annotator for one language were assessed by a different annotator when evaluating that topic on a different language. There were 172 total runs submitted by twelve teams.