The Cross-Lingual Arabic Information REtrieval (CLAIRE) System

TL;DR

The CLAIRE system enables cross-lingual Arabic information retrieval using English-Arabic word embeddings, simplifying the pipeline and avoiding translation errors, with promising initial results on Arabic news data.

Contribution

This paper introduces an end-to-end cross-lingual retrieval system based on cross-lingual word embeddings, avoiding complex translation models and supporting various neural retrieval methods.

Findings

Promising retrieval performance on Arabic news collection

Simplifies cross-lingual retrieval pipeline

Avoids translation-related errors

Abstract

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.