%0 Conference Proceedings %T Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval %A Ren, Houxing %A Shou, Linjun %A Wu, Ning %A Gong, Ming %A Jiang, Daxin %Y Goldberg, Yoav %Y Kozareva, Zornitsa %Y Zhang, Yue %S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates %F ren-etal-2022-empowering %X In monolingual dense retrieval, lots of works focus on how to distill knowledge from cross-encoder re-ranker to dual-encoder retriever and these methods achieve better performance due to the effectiveness of cross-encoder re-ranker. However, we find that the performance of the cross-encoder re-ranker is heavily influenced by the number of training samples and the quality of negative samples, which is hard to obtain in the cross-lingual setting. In this paper, we propose to use a query generator as the teacher in the cross-lingual setting, which is less dependent on enough training samples and high-quality negative samples. In addition to traditional knowledge distillation, we further propose a novel enhancement method, which uses the query generator to help the dual-encoder align queries from different languages, but does not need any additional parallel sentences. The experimental results show that our method outperforms the state-of-the-art methods on two benchmark datasets. %R 10.18653/v1/2022.emnlp-main.203 %U https://aclanthology.org/2022.emnlp-main.203 %U https://doi.org/10.18653/v1/2022.emnlp-main.203 %P 3107-3121