| Challenge: | Existing models for integrating factual knowledge into pre-trained language models are shallow, static, and separately pre-train entities. |
| Approach: | They propose a method which integrates knowledge contexts from large-scale knowledge bases into a unified data structure. |
| Outcome: | The proposed model outperforms existing models on knowledge-driven tasks and knowledge probing tasks. |
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| Challenge: | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of their capabilities. |
| Approach: | They investigate BERT's ability to encode various commonsense features in its embedding space, but are still deficient in many areas. |
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An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks (2022.emnlp-main)
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| Challenge: | Recent work attempts to explicitly incorporate human-defined linguistic priors into fine-tuning tasks. |
| Approach: | They replace parsed graphs or trees with trivial ones to investigate linguistic priors . they propose to use trivial graphs as baselines to design advanced knowledge fusion methods . |
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BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge representation learning methods do not use graph contextualized knowledge. |
| Approach: | They propose to model subgraphs in a medical KG and integrate it with a pre-trained language model to do knowledge generalization. |
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Knowledge Enhanced Contextual Word Representations (D19-1)
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Matthew E. Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, Noah A. Smith
| Challenge: | Existing methods to embed knowledge bases into large pre-training models do not contain any explicit grounding to real world entities and are difficult to recover factual knowledge. |
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A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
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Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
| Approach: | They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models. |
| Outcome: | Extensive experiments show that the proposed framework can improve results over existing models. |
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge (2022.naacl-main)
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| Challenge: | Existing evidence suggests that pre-trained Transformers encode commonsense knowledge . however, the extent to which this knowledge is acquired is unclear . |
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Contextual Representation Learning beyond Masked Language Modeling (2022.acl-long)
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| Challenge: | masked language models adopt sampled embeddings as anchors to estimate and inject contextual semantics to representations. |
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Infusing Finetuning with Semantic Dependencies (2021.tacl-1)
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| Challenge: | Several diagnostics help to localize the benefits of our approach. |
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Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction (2022.acl-srw)
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| Challenge: | Existing models that perform explicit on-task training of graph embeddings are inadequate. |
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