Visual Pivoting for (Unsupervised) Entity Alignment
Authors: Fangyu Liu, Muhao Chen, Dan Roth, Nigel Collier4257-4266
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark data sets DBP15k and DWY15k show that EVA offers state-of-the-art performance on both monolingual and cross-lingual entity alignment tasks. Furthermore, we discover that images are particularly useful to align long-tail KG entities, which inherently lack the structural contexts that are necessary for capturing the correspondences. Code release: https://github.com/cambridgeltl/eva; project page: http://cogcomp.org/page/publication view/927. In this section, we conduct experiments on two benchmark data sets ( 4.1), under both semiand unsupervised settings ( 4.2). We also provide detailed ablation studies on different model components ( 4.3), and study the impact of incorporating visual representations on long-tail entities ( 4.4). |
| Researcher Affiliation | Academia | 1 Language Technology Lab, TAL, University of Cambridge, UK 2 Department of Computer and Information Science, University of Pennsylvania, USA 3 Viterbi School of Engineering, University of Southern California, USA |
| Pseudocode | Yes | Algorithm 1: Visual pivot induction. |
| Open Source Code | Yes | Code release: https://github.com/cambridgeltl/eva; project page: http://cogcomp.org/page/publication view/927. |
| Open Datasets | Yes | The experiments are conducted on DBP15k (Sun, Hu, and Li 2017) and DWY15k (Guo, Sun, and Hu 2019). |
| Dataset Splits | No | The paper states 'using 30% of the EA labels for training' and discusses test sets, but does not explicitly provide percentages or counts for a separate validation split or refer to a standard validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam W', 'RESNET-152', and 'FASTTEXT' but does not provide specific version numbers for them within the main text. |
| Experiment Setup | Yes | The GCN has two layers with input, hidden and output dimensions of 400, 400, 200 respectively. Attribute and relation features are mapped to 100-d. Images are transformed to 2048-d features by RESNET and then mapped to 200-d. For model variants without IL, training is limited to 500 epochs. Otherwise, after the first 500 epochs, IL is conducted for another 500 epochs with the configurations Ke = 5, Ks = 10 as described in 3.2. We train all models using a batch size of 7,500. The models are optimised using Adam W (Loshchilov and Hutter 2019) with a learning rate of 5e-4 and a weight decay of 1e-2. |