The Cross-Lingual Arabic Information REtrieval (CLAIRE) System
This work addresses cross-lingual retrieval for users with passive Arabic understanding, providing an incremental improvement by simplifying existing methods.
The paper tackles the challenge of cross-lingual information retrieval between English and Arabic by proposing the CLAIRE system, which uses cross-lingual word embeddings to simplify the pipeline and avoid machine translation errors, with early empirical results showing promising performance on an Arabic news collection.
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.