Papers › KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on...
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs
Yinquan Lu, Haonan Lu, Guirong Fu, Qun Liu
Incorporating factual knowledge into pre-trained language models (PLM) such as BERT is an emerging trend in recent NLP studies. However, most of the existing methods combine the external knowledge integration module with a modified pre-training loss and re-implement the pre-training process on the large-scale corpus. Re-pretraining these models is usually resource-consuming, and difficult to adapt to another domain with a different knowledge graph (KG). Besides, those works either cannot embed knowledge context dynamically according to textual context or struggle with the knowledge ambiguity issue. In this paper, we propose a novel knowledge-aware language model framework based on fine-tuning process, which equips PLM with a unified knowledge-enhanced text graph that contains both text and multi-relational sub-graphs extracted from KG. We design a hierarchical relational-graph-based message passing mechanism, which can allow the representations of injected KG and text to mutually update each other and can dynamically select ambiguous mentioned entities that share the same text. Our empirical results show that our model can efficiently incorporate world knowledge from KGs into existing language models such as BERT, and achieve significant improvement on the machine reading comprehension (MRC) task compared with other knowledge-enhanced models.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Common Sense Reasoning | ReCoRD | KELM (finetuning RoBERTa-large based single model) | EM | 89.1 | #11 of 45 | Archive leaderboard | report |
| Common Sense Reasoning | ReCoRD | KELM (finetuning RoBERTa-large based single model) | F1 | 89.6 | #11 of 45 | Archive leaderboard | report |
| Common Sense Reasoning | ReCoRD | KELM (finetuning BERT-large based single model) | EM | 76.2 | #21 of 45 | Archive leaderboard | report |
| Common Sense Reasoning | ReCoRD | KELM (finetuning BERT-large based single model) | F1 | 76.7 | #21 of 45 | Archive leaderboard | report |
| Question Answering | COPA | KELM (finetuning BERT-large based single model) | Accuracy | 78.0 | #39 of 60 | Archive leaderboard | report |
| Question Answering | MultiRC | KELM (finetuning BERT-large based single model) | EM | 27.2 | #15 of 30 | Archive leaderboard | report |
| Question Answering | MultiRC | KELM (finetuning BERT-large based single model) | F1 | 70.8 | #15 of 30 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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