  In this work, we explore a Multilingual Information Retrieval (MLIR) task,
where the collection includes documents in multiple languages. We demonstrate
that applying state-of-the-art approaches developed for cross-lingual
information retrieval to MLIR tasks leads to sub-optimal performance. This is
due to the heterogeneous and imbalanced nature of multilingual collections --
some languages are better represented in the collection and some benefit from
large-scale training data. To address this issue, we present KD-SPD, a novel
soft prompt decoding approach for MLIR that implicitly "translates" the
representation of documents in different languages into the same embedding
space. To address the challenges of data scarcity and imbalance, we introduce a
knowledge distillation strategy. The teacher model is trained on rich English
retrieval data, and by leveraging bi-text data, our distillation framework
transfers its retrieval knowledge to the multilingual document encoder.
Therefore, our approach does not require any multilingual retrieval training
data. Extensive experiments on three MLIR datasets with a total of 15 languages
demonstrate that KD-SPD significantly outperforms competitive baselines in all
cases. We conduct extensive analyses to show that our method has less language
bias and better zero-shot transfer ability towards new languages.
