  A dense passage retrieval system can serve as the initial stages of
information retrieval, selecting the most relevant text passages for downstream
tasks. In this work we conducted experiments with the goal of finding how much
the quality of a multilingual retrieval could be degraded if the query part of
a dual encoder is tuned on an English-only dataset (assuming scarcity of
cross-lingual samples for the targeted domain or task). Specifically, starting
with a high quality multilingual embedding model, we observe that an
English-only tuning may not only preserve the original quality of the
multilingual retrieval, but even improve it.
