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A Study of Neural Matching Models for Cross-lingual IR
May 26, 2020 Β· Declared Dead Β· π Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
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Authors
Puxuan Yu, James Allan
arXiv ID
2005.12994
Category
cs.IR: Information Retrieval
Cross-listed
cs.CL
Citations
27
Venue
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Last Checked
3 months ago
Abstract
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.
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