  As a crucial role in cross-language information retrieval (CLIR), query
translation has three main challenges: 1) the adequacy of translation; 2) the
lack of in-domain parallel training data; and 3) the requisite of low latency.
To this end, existing CLIR systems mainly exploit statistical-based machine
translation (SMT) rather than the advanced neural machine translation (NMT),
limiting the further improvements on both translation and retrieval quality. In
this paper, we investigate how to exploit neural query translation model into
CLIR system. Specifically, we propose a novel data augmentation method that
extracts query translation pairs according to user clickthrough data, thus to
alleviate the problem of domain-adaptation in NMT. Then, we introduce an
asynchronous strategy which is able to leverage the advantages of the real-time
in SMT and the veracity in NMT. Experimental results reveal that the proposed
approach yields better retrieval quality than strong baselines and can be well
applied into a real-world CLIR system, i.e. Aliexpress e-Commerce search
engine. Readers can examine and test their cases on our website:
https://aliexpress.com .
