Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment
Authors: Luyao Wang, Pengnian Qi, Xigang Bao, Chunlai Zhou, Biao Qin
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two MMEA datasets demonstrate the effectiveness of our PCMEA, which yields state-of-the-art performance. |
| Researcher Affiliation | Academia | School of Information, Renmin University of China {wangluyao123, pengnianqi, baoxigang, czhou, qinbiao} @ruc.edu.cn |
| Pseudocode | No | The paper describes its methods and components with equations but does not include explicit pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper mentions that pre-trained language models are downloaded from Hugging Face but does not provide any statement or link for the open-source code of their proposed PCMEA method. |
| Open Datasets | Yes | Two cross-KG EA datasets are adopted for evaluation, including FB15K-DB15K and FB15K-YAGO15K, which are the most representative datasets in MMEA task (Chen et al. 2020, 2022a; Lin et al. 2022). |
| Dataset Splits | No | The paper mentions "different proportions of entity alignment seeds, i.e., 2:8, 5:5, and 8:2" for training/testing, but does not explicitly specify a separate validation dataset split. |
| Hardware Specification | Yes | All experiments were conducted on a server with two GPUs (NVIDIA-SMI 3090). |
| Software Dependencies | No | The paper mentions PyTorch and specific pre-trained language models (Bert, T5, RoBERTa, Albert, ChatGLM-6B, LLaMA-7B) but does not provide specific version numbers for PyTorch or the PLMs, only stating "all of them are base version". |
| Experiment Setup | Yes | The main hyper-parameters in our method are momentum coefficient κ, momentum network update span ρ, and time of changing training strategy ts. For momentum coefficient κ, a proper large κ (e.g. 0.999) bring better stability and accuracy in Hits@1 and MRR, illustrating momentum-based contrast learning is more effective than just contrast learning. Varying time span ρ shows little difference. For time ts of changing training strategy, time ts obvious effects the surge in Hits@1 after changing the strategy, with ts = 500 allowing faster convergence. |