Papers by Carl Allen
Adapters for Enhanced Modeling of Multilingual Knowledge and Text (2022.findings-emnlp)
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| Challenge: | Large language models learn facts from text corpora, but knowledge graphs contain facts in an explicit triple format, restricting their research and application. |
| Approach: | They propose to enhance multilingual language models with knowledge from multilingual knowledge graphs . they propose to use cross-lingual entity alignment and facts from MLKGs to improve performance . |
| Outcome: | The proposed model improves MLLMs with cross-lingual entity alignment and facts from multilingual knowledge graphs for many languages while maintaining performance on other general language tasks. |
TuckER: Tensor Factorization for Knowledge Graph Completion (D19-1)
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| Challenge: | Knowledge graphs contain only a small subset of all possible facts . link prediction is a task of inferring missing facts based on existing facts - knowledge graphs are expensive and lack of information is needed to add new information. |
| Approach: | They propose a linear model based on Tucker decomposition of knowledge graph triples . they show that the model is expressive and has sufficient bounds on its embedding dimensionalities . |
| Outcome: | The proposed model outperforms state-of-the-art models across standard datasets and acts as a strong baseline for more elaborate models. |