PaperLM: A Pre-trained Model for Hierarchical Examination Paper Representation Learning-Reference-Cited by-同舟云学术

PaperLM: A Pre-trained Model for Hierarchical Examination Paper Representation Learning

Published:2023-10-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Shan Minghui¹^ORCID,Ma Yixiao¹^ORCID,Ruan Shulan¹^ORCID,Cao Zhi¹^ORCID,Tong Shiwei¹^ORCID,Liu Qi¹^ORCID,Su Yu²^ORCID,Wang Shijin³^ORCID

Affiliation:

1. University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence, Hefei, China

2. Hefei Normal University,Institute of Artificial Intelligence & Hefei Comprehensive National Science Center, Hefei, China

3. iFLYTEK AI Research (Central China) & State Key Laboratory of Cognitive Intelligence, Hefei, China

Funder

the National Key Research and Development Program of China

the University Synergy Innovation Program of Anhui Province

the Laboratory of Cognitive Intelligence

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3615003

Reference44 articles.

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2. Learning ontology resolution for document representation and its applications in text mining

3. Wasserstein Contrastive Representation Distillation

4. Xiang Deng , Prashant Shiralkar , Colin Lockard , Binxuan Huang , and Huan Sun . 2022. DOM-LM: Learning Generalizable Representations for HTML Documents. arXiv preprint arXiv:2201.10608 ( 2022 ). Xiang Deng, Prashant Shiralkar, Colin Lockard, Binxuan Huang, and Huan Sun. 2022. DOM-LM: Learning Generalizable Representations for HTML Documents. arXiv preprint arXiv:2201.10608 (2022).

5. Item Rearrangement Under Typical Test Conditions