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A Study of Neural Matching Models for Cross-lingual IR

arXiv 2005.12994 ↗First posted 26 May 2020cs.IR cs.CLcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2020.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 3; 2024: 0; 2025: 1.

12 papers.