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pro vyhledávání: '"Shen, Jia Tracy"'
Autor:
Shen, Jia Tracy, Lee, Dongwon
The issue of missing data poses a great challenge on boosting performance and application of deep learning models in the {\em Knowledge Tracing} (KT) problem. However, there has been the lack of understanding on the issue in the literature. %are not
Externí odkaz:
http://arxiv.org/abs/2302.12910
In online job marketplaces, it is important to establish a well-defined job title taxonomy for various downstream tasks (e.g., job recommendation, users' career analysis, and turnover prediction). Job Title Normalization (JTN) is such a cleaning step
Externí odkaz:
http://arxiv.org/abs/2202.10739
Autor:
Shen, Jia Tracy, Yamashita, Michiharu, Prihar, Ethan, Heffernan, Neil, Wu, Xintao, Graff, Ben, Lee, Dongwon
Since the introduction of the original BERT (i.e., BASE BERT), researchers have developed various customized BERT models with improved performance for specific domains and tasks by exploiting the benefits of transfer learning. Due to the nature of ma
Externí odkaz:
http://arxiv.org/abs/2106.07340
Autor:
Shen, Jia Tracy, Yamashita, Michiharu, Prihar, Ethan, Heffernan, Neil, Wu, Xintao, McGrew, Sean, Lee, Dongwon
Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers. However, manually labeling educational content is labor intensive and error-prone. To address this challenge, prior research
Externí odkaz:
http://arxiv.org/abs/2105.11343
One of the most essential tasks needed for various downstream tasks in career analytics (e.g., career trajectory analysis, job mobility prediction, and job recommendation) is Job Title Mapping (JTM), where the goal is to map user-created (noisy and n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9539e3e1b242c3fddfbfa60f063f094c
http://arxiv.org/abs/2202.10739
http://arxiv.org/abs/2202.10739