A Study of Neural Matching Models for Cross-lingual IR
Puxuan Yu, James Allan
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.
Topics & Concepts
Computer scienceClefMatching (statistics)Artificial intelligenceSimilarity (geometry)Natural language processingWord (group theory)Artificial neural networkInformation retrievalImage (mathematics)MathematicsEconomicsGeometryTask (project management)StatisticsManagementTopic ModelingSemantic Web and OntologiesNatural Language Processing Techniques