  Effective cross-lingual dense retrieval methods that rely on multilingual
pre-trained language models (PLMs) need to be trained to encompass both the
relevance matching task and the cross-language alignment task. However,
cross-lingual data for training is often scarcely available. In this paper,
rather than using more cross-lingual data for training, we propose to use
cross-lingual query generation to augment passage representations with queries
in languages other than the original passage language. These augmented
representations are used at inference time so that the representation can
encode more information across the different target languages. Training of a
cross-lingual query generator does not require additional training data to that
used for the dense retriever. The query generator training is also effective
because the pre-training task for the generator (T5 text-to-text training) is
very similar to the fine-tuning task (generation of a query). The use of the
generator does not increase query latency at inference and can be combined with
any cross-lingual dense retrieval method. Results from experiments on a
benchmark cross-lingual information retrieval dataset show that our approach
can improve the effectiveness of existing cross-lingual dense retrieval
methods. Implementation of our methods, along with all generated query files
are made publicly available at https://github.com/ielab/xQG4xDR.
