  Query translation (QT) is a key component in cross-lingual information
retrieval system (CLIR). With the help of deep learning, neural machine
translation (NMT) has shown promising results on various tasks. However, NMT is
generally trained with large-scale out-of-domain data rather than in-domain
query translation pairs. Besides, the translation model lacks a mechanism at
the inference time to guarantee the generated words to match the search index.
The two shortages of QT result in readable texts for human but inadequate
candidates for the downstream retrieval task. In this paper, we propose a novel
approach to alleviate these problems by limiting the open target vocabulary
search space of QT to a set of important words mined from search index
database. The constraint translation candidates are employed at both of
training and inference time, thus guiding the translation model to learn and
generate well performing target queries. The proposed methods are exploited and
examined in a real-word CLIR system--Aliexpress e-Commerce search engine.
Experimental results demonstrate that our approach yields better performance on
both translation quality and retrieval accuracy than the strong NMT baseline.
