Papers with knowledge embedding methods
Differentiating Concepts and Instances for Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing knowledge graph embedding methods encode concepts and instances as vectors in a low-dimensional space, ignoring the difference between concepts and instance. |
| Approach: | They propose a knowledge graph embedding model that separates concepts from instances by differentiating concepts and instances. |
| Outcome: | The proposed model outperforms state-of-the-art methods on link prediction and triple classification tasks on YAGO dataset. |
Generic Overgeneralization in Pre-trained Language Models (2022.coling-1)
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| Challenge: | Generic statements such as "ducks lay eggs" are perceived as false universally . however, universally quantified statements such "all tigers have stripes" should be perceived as true . |
| Approach: | They investigate the generic overgeneralization effect in pre-trained language models . they show that pre-trainers tend to treat quantified generic statements as if they were true . |
| Outcome: | The proposed model reduces, but does not eliminate, generic overgeneralization bias . the model can be used to inject factual knowledge about kinds into pre-trained models . |
Can Pre-trained Language Models Interpret Similes as Smart as Human? (2022.acl-long)
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| Challenge: | Simile interpretation is a crucial task in natural language processing. |
| Approach: | They propose a task to let PLMs infer the shared properties of similes by probing textual corpora and human-designed questions. |
| Outcome: | The proposed task outperforms pre-trained language models on simile interpretation tasks while still underperforming humans. |