Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding (2023.emnlp-main)
Copied to clipboard
Taolin Zhang, Ruyao Xu, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian
| Challenge: | Existing methods for pre-training KEPLMs with relational triples are difficult to adapt to close domains due to the lack of sufficient domain graph semantics. |
| Approach: | They propose a Knowledge-enhanced language representation learning framework for various closed domains that captures the implicit graph structure among the entities. |
| Outcome: | The proposed framework outperforms existing methods for pre-training KEPLMs in closed domains significantly. |
Similar Papers
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | General pre-trained language models (PLMs) leverage relation triples from knowledge graphs (KGs) and integrate external data sources into language models via self-supervised learning. |
| Approach: | They propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL) to detect positions for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
| Outcome: | The proposed model can detect essential positions in texts for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
Copied to clipboard
| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
| Outcome: | The proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. |
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for language model pretraining use limited knowledge graph data for knowledge-intensive tasks. |
| Approach: | They propose to make better use of multilingual annotations and language agnostic properties of KG triples for pretraining LMs. |
| Outcome: | The proposed models show significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks. |
KALA: Knowledge-Augmented Language Model Adaptation (2022.naacl-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have proved to be effective on various natural language understanding tasks. |
| Approach: | They propose a domain adaption framework which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts. |
| Outcome: | The proposed framework outperforms adaptive pre-training on question answering and named entity recognition tasks on multiple datasets across different domains. |
Path-enhanced Pre-trained Language Model for Knowledge Graph Completion (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models have achieved remarkable knowledge graph completion (KGC) success. |
| Approach: | They propose a path-enhanced pre-trained language model-based knowledge graph completion method which uses multi-view generation to infer missing facts in triple-level and path-level simultaneously. |
| Outcome: | The proposed method significantly improves the performance of the knowledge graph completion task. |
Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)
Copied to clipboard
| Challenge: | Text-based knowledge graph completion methods neglect knowledge contexts in inferring process. |
| Approach: | They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion. |
| Outcome: | The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets. |
Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Recent trends in NLP utilize knowledge graphs to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked. |
| Approach: | They propose to use graph-aware neighborhood contrastive learning methodology SciNCL to enhance pretrained language models by incorporating additional knowledge from graph structures. |
| Outcome: | The proposed graph-aware neighborhood contrastive learning methodology outperforms a state-of-the-art mE5-large text encoder on the process industry text embedding benchmark while having 3 times fewer parameters. |
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for incorporating external knowledge into language models do not prioritize learning embeddings for entity-related tokens. |
| Approach: | They propose a framework for incorporating external knowledge into pre-training models that utilize entity-related tokens. |
| Outcome: | The proposed framework reduces pre-training time by 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks. |
Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to augment LLMs with Knowledge Graphs (KGs) Knowledge-intensive tasks are prone to errors and require a large amount of knowledge to be understood. |
| Approach: | They propose a framework for augmenting LLMs through Knowledge Graphs (KGs) they propose KGs can be used to enhance performance in knowledge-intensive tasks . |
| Outcome: | Experimental results show that a small domain-specific KG can benefit from a performance boost in downstream tasks when linked to a substantial general-purpose KG. |
Knowledge Graph-Enhanced Large Language Models via Path Selection (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown unprecedented performance in various real-world applications, but they are known to generate factually inaccurate outputs. |
| Approach: | They propose a framework to integrate external knowledge extracted from Knowledge Graphs (KGs) they propose to generate scores for knowledge paths with input texts via latent semantic matching. |
| Outcome: | Experiments on real-world datasets validate the effectiveness of a framework to extract knowledge from Knowledge Graphs (KGs) incorporating external knowledge has become a promising strategy to improve the factual accuracy of LLM-generated outputs. |