  Despite advances in neural machine translation, cross-lingual retrieval tasks
in which queries and documents live in different natural language spaces remain
challenging. Although neural translation models may provide an intuitive
approach to tackle the cross-lingual problem, their resource-consuming training
and advanced model structures may complicate the overall retrieval pipeline and
reduce users engagement. In this paper, we build our end-to-end Cross-Lingual
Arabic Information REtrieval (CLAIRE) system based on the cross-lingual word
embedding where searchers are assumed to have a passable passive understanding
of Arabic and various supporting information in English is provided to aid
retrieval experience. The proposed system has three major advantages: (1) The
usage of English-Arabic word embedding simplifies the overall pipeline and
avoids the potential mistakes caused by machine translation. (2) Our CLAIRE
system can incorporate arbitrary word embedding-based neural retrieval models
without structural modification. (3) Early empirical results on an Arabic news
collection show promising performance.
