  Multilingual information retrieval (IR) is challenging since annotated
training data is costly to obtain in many languages. We present an effective
method to train multilingual IR systems when only English IR training data and
some parallel corpora between English and other languages are available. We
leverage parallel and non-parallel corpora to improve the pretrained
multilingual language models' cross-lingual transfer ability. We design a
semantic contrastive loss to align representations of parallel sentences that
share the same semantics in different languages, and a new language contrastive
loss to leverage parallel sentence pairs to remove language-specific
information in sentence representations from non-parallel corpora. When trained
on English IR data with these losses and evaluated zero-shot on non-English
data, our model demonstrates significant improvement to prior work on retrieval
performance, while it requires much less computational effort. We also
demonstrate the value of our model for a practical setting when a parallel
corpus is only available for a few languages, but a lack of parallel corpora
resources persists for many other low-resource languages. Our model can work
well even with a small number of parallel sentences, and be used as an add-on
module to any backbones and other tasks.
