Papers by Canran Xu
Relation Embedding with Dihedral Group in Knowledge Graph (P19-1)
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| Challenge: | Existing models for link prediction fail to capture relation compositions, resulting in lack of interpretability for reasoning on incomplete knowledge graph (KG). |
| Approach: | They propose a new model that learns knowledge graph embeddings that can capture relation compositions by nature and reduces the solution space drastically. |
| Outcome: | The proposed model outperforms existing models and is comparable to or better than deep learning models such as ConvE. |
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification (2024.eacl-long)
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| Challenge: | Existing methods to improve pre-trained language models for many-class classification suffer from verbalizer ambiguity . a significant disparity exists between the pre-training and fine-tuning stages of the model . |
| Approach: | They propose a method to tune pre-trained language models to a broad spectrum of tasks . they use an instance-dependent soft prefix to complement language verbalizers in many-class classification . |
| Outcome: | The proposed method outperforms baselines on many-class datasets. |