K-LM: Knowledge Augmenting in Language Models Within the Scholarly Domain
The use of superior algorithms and complex architectures in language models have successfully imparted human-like abilities to machines for specific tasks. But two significant constraints, the available training data size and the understanding of domain-specific context, hamper the pre-trained langu...
Main Authors: | Vivek Kumar, Diego Reforgiato Recupero, Rim Helaoui, Daniele Riboni |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9866735/ |
Similar Items
-
Scholarly knowledge graphs through structuring scholarly communication: a review
by: Shilpa Verma, et al.
Published: (2022-08-01) -
Integrating Conversational Agents and Knowledge Graphs Within the Scholarly Domain
by: Antonello Meloni, et al.
Published: (2023-01-01) -
Advances in Knowledge Graph Embedding Based on Graph Neural Networks
by: YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao
Published: (2023-08-01) -
IPPT4KRL: Iterative Post-Processing Transfer for Knowledge Representation Learning
by: Weihang Zhang, et al.
Published: (2023-01-01) -
Augmenting Embedding Projection With Entity Descriptions for Knowledge Graph Completion
by: Junfan Chen, et al.
Published: (2021-01-01)