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Despite advances in neural machine translation, cross-lingual retrieval tasksin which queries and documents live in different natural language spaces remainchallenging. Although neural translation models may provide an intuitiveapproach to tackle the cross-lingual problem, their resource-consuming trainingand advanced model structures may complicate the overall retrieval pipeline andreduce users engagement. In this paper, we build our end-to-end Cross-LingualArabic Information REtrieval (CLAIRE) system based on the cross-lingual wordembedding where searchers are assumed to have a passable passive understandingof Arabic and various supporting information in English is provided to aidretrieval experience. The proposed system has three major advantages: (1) Theusage of English-Arabic word embedding simplifies the overall pipeline andavoids the potential mistakes caused by machine translation. (2) Our CLAIREsystem can incorporate arbitrary word embedding-based neural retrieval modelswithout structural modification. (3) Early empirical results on an Arabic newscollection show promising performance.

The Cross-Lingual Arabic Information REtrieval (CLAIRE) System