# A Study of Neural Matching Models for Cross-lingual IR

arXiv: https://arxiv.org/abs/2005.12994 · HTML: https://mlsanity.com/paper/2005.12994/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 12 of 12)

- [Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models](https://mlsanity.com/paper/2201.08471/) (2022)
- [On Cross-Lingual Retrieval with Multilingual Text Encoders](https://mlsanity.com/paper/2112.11031/) (2021)
- [Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval](https://mlsanity.com/paper/2101.08370/) (2021)
- [Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation](https://mlsanity.com/paper/2301.12566/) (2023)
- [C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval](https://mlsanity.com/paper/2204.11989/) (2022)
- [Soft Prompt Decoding for Multilingual Dense Retrieval](https://mlsanity.com/paper/2305.09025/) (2023)
- [Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval](https://mlsanity.com/paper/2109.02789/) (2021)
- [Cross-Lingual Training with Dense Retrieval for Document Retrieval](https://mlsanity.com/paper/2109.01628/) (2021)
- [Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters](https://mlsanity.com/paper/2212.10448/) (2022)
- [Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval](https://mlsanity.com/paper/2304.01019/) (2023)
- [Multilingual Generative Retrieval via Cross-lingual Semantic Compression](https://mlsanity.com/paper/2510.07812/) (2025)
- [Cross-language Information Retrieval](https://mlsanity.com/paper/2111.05988/) (2021)

## Abstract (arXiv)

In this study, we investigate interaction-based neural matching models for ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs). With experiments conducted on the CLEF collection over four language pairs, we evaluate and provide insight into different neural model architectures, different ways to represent query-document interactions and word-pair similarity distributions in CLIR. This study paves the way for learning an end-to-end CLIR system using CLWEs.