Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In this work, we show that the effectiveness of zero-shot rankers diminishes when queries and documents are present in different languages. Motivated by this, we propose to train ranking models on artificially code-switched data instead, which we generate by utilizing bilingual lexicons. To this end, we experiment with lexicons induced from (1) cross-lingual word embeddings and (2) parallel Wikipedia page titles. We use the mMARCO dataset to extensively evaluate reranking models on 36 language pairs spanning Monolingual IR (MoIR), Cross-lingual IR (CLIR), and Multilingual IR (MLIR). Our results show that code-switching can yield consistent and substantial gains of 5.1 MRR@10 in CLIR and 3.9 MRR@10 in MLIR, while maintaining stable performance in MoIR. Encouragingly, the gains are especially pronounced for distant languages (up to 2x absolute gain). We further show that our approach is robust towards the ratio of code-switched tokens and also extends to unseen languages. Our results demonstrate that training on code-switched data is a cheap and effective way of generalizing zero-shot rankers for cross-lingual and multilingual retrieval.

翻译：将信息检索（IR）模型以零样本方式从高资源语言（通常为英语）迁移至其他语言已成为广泛采用的方法。本研究表明，当查询与文档分属不同语言时，零样本排序器的有效性会降低。为此，我们提出利用双语词典生成人工编码混合数据，并基于此训练排序模型。我们尝试使用从（1）跨语言词嵌入和（2）平行维基百科页面标题中诱导出的词典。基于mMARCO数据集，我们在涵盖单语IR（MoIR）、跨语言IR（CLIR）和多语言IR（MLIR）的36个语言对中广泛评估了重排序模型。结果显示，编码混合策略在CLIR中可带来5.1 MRR@10的显著且稳定的提升，在MLIR中提升3.9 MRR@10，同时保持MoIR性能稳定。值得注意的是，该增益对远缘语言尤为显著（绝对增益高达2倍）。我们进一步证明该方法对编码混合标记比例具有鲁棒性，并可扩展至未见语言。实验结果表明，基于编码混合数据训练是泛化跨语言和多语言检索的零样本排序器的一种低成本且高效的方法。