Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement
Authors: Fuwei Zhang, Zhao Zhang, Xiang Ao, Dehong Gao, Fuzhen Zhuang, Yi Wei, Qing He4345-4353
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results demonstrate that HIKE achieves substantial improvements over state-of-the-art competitors. |
| Researcher Affiliation | Collaboration | 1 Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China 4 Institute of Intelligent Computing Technology, Suzhou, CAS 5 Alibaba Group, Hangzhou, China 6 Institute of Artificial Intelligence, Beihang University, Beijing 100191, China 7 SKLSDE, School of Computer Science, Beihang University, Beijing 100191, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code. |
| Open Datasets | Yes | We evaluate the HIKE model in a public CLIR dataset CLIRMatrix (Sun and Duh 2020). Specifically, we use the MULTI-8 set in CLIRMatrix, in which queries and documents are jointly aligned in 8 different languages. |
| Dataset Splits | Yes | The training sets of every language pair contain 10,000 queries, while the validation and the test sets contain 1,000 queries. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'multilingual BERT' and 'BERT-base, multilingual cased' but does not provide specific version numbers for software dependencies or frameworks. |
| Experiment Setup | Yes | In the training stage, the number of heads for the multi-head attention mechanism in knowledge-level fusion is set to 6. The learning rates are divided into two parts: the BERT lr1 and the other modules lr2. And we set lr1 to 1e-5 and lr2 to 1e-3. We set the number of neighboring entities in KG as 3. We randomly sample 1600 query-document pairs as our training data per epoch. The maximum training epochs are set to 15. |