TY - JOUR AU - Zhang, Fuwei AU - Zhang, Zhao AU - Ao, Xiang AU - Gao, Dehong AU - Zhuang, Fuzhen AU - Wei, Yi AU - He, Qing PY - 2022/06/28 Y2 - 2024/03/28 TI - Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 4 SE - AAAI Technical Track on Data Mining and Knowledge Management DO - 10.1609/aaai.v36i4.20355 UR - https://ojs.aaai.org/index.php/AAAI/article/view/20355 SP - 4345-4353 AB - Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user’s query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. It is regarded as a “silver bullet” to simultaneously perform explicit alignment between queries and documents and also broaden the representations of queries. And we propose a model named CLIR with HIerarchical Knowledge Enhancement (HIKE) for our task. The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism. Particularly, HIKE first integrates the entities and their neighborhood in KG into query representations with a knowledge-level fusion, then combines the knowledge from both source and target languages to further mitigate the linguistic gap with a language-level fusion. Finally, experimental results demonstrate that HIKE achieves substantial improvements over state-of-the-art competitors. ER -