A Study of Neural Matching Models for Cross-lingual IR

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.

4 pages, 1 figure, accepted at SIGIR'20

References in corpus (3)

• A Deep Relevance Matching Model for Ad-hoc Retrieval
• End-to-End Neural Ad-hoc Ranking with Kernel Pooling
• Word Translation Without Parallel Data

Cited by in corpus (7)

• C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval
• Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation
• Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval
• Soft Prompt Decoding for Multilingual Dense Retrieval
• Cross-Lingual Training with Dense Retrieval for Document Retrieval
• Searching Personal Collections
• Cross-language Information Retrieval