Materials for AACL2022 tutorial: Efficient and Robust Knowledge Graph Construction

Knowledge graph construction which aims to extract knowledge from the text corpus has appealed to the NLP community researchers. Previous decades have witnessed the remarkable progress of knowledge graph construction on the basis of neural models; however, those models often cost massive computation or labeled data resources and suffer from unstable inference accounting for biased or adversarial samples. Recently, numerous approaches have been explored to mitigate the efficiency and robustness issues for knowledge graph construction, such as prompt learning and adversarial training. In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for efficient and robust knowledge graph construction. Additionally, our goal is to provide a systematic and up-to-date overview of these methods and reveal new research opportunities to the audience.

@inproceedings{zhang2022efficient,
  title={Efficient and Robust Knowledge Graph Construction},
  author={Zhang, Ningyu and Gui, Tao and Nan, Guoshun},
  booktitle={Proceedings of the 2st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts},
  year={2022}
}

1. Slides [Introduction] [EfficientKGC] [RobustKGC] [Conclusion]

2. Video [AllParts]

3. Survey:

Knowledge Graph Construction

• A Survey on Deep Learning for Named Entity Recognition (TKDE, 2022) [paper]
• A Survey on Recent Advances in Named Entity Recognition from Deep Learning Models (COLING 2018) [paper]
• A Survey on Neural Relation Extraction (Science China Technological Sciences, 2020) [paper]
• What is Event Knowledge Graph: A Survey (TKDE, 2022) [paper]
• Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective (on arxiv, 2022) [paper]

Efficient NLP

• A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (NAACL 2021) [paper]
• Few-Shot Named Entity Recognition: An Empirical Baseline Study (EMNLP 2021) [paper]
• A Survey on Low-Resource Neural Machine Translation (IJCAI 2021) [paper]
• Efficient Methods for Natural Language Processing: A Survey (on arxiv 2022) [paper]

Low-resource Learning

• Generalizing from a Few Examples: A Survey on Few-shot Learning (ACM Computing Surveys, 2021) [paper]
• Knowledge-aware Zero-Shot Learning: Survey and Perspective (IJCAI 2021) [paper]
• Low-resource Learning with Knowledge Graphs: A Comprehensive Survey (2021) [paper]

4. Reading list:

• Template-free prompt tuning for few-shot NER, in NAACL 2022. [pdf]
• Reasoning with Latent Structure Refinement for Document-Level Relation Extraction, in ACL 2020. [pdf]
• KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction, in WWW 2022. [pdf]
• Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning, in NeurIPS 2022. [pdf]

Ningyu Zhang           Tao Gui               Guoshun Nan

Ningyu Zhang is an associate professor at Zhejiang University, his main research interests are knowledge graph, NLP, etc. He has published papers in top international academic conferences and journals such as NeurIPS/ICLR/WWW/KDD/WSDM/AAAI/IJCAI/ACL/ENNLP/NAACL/COLING/SIGIR/TASLP/ESWA/KBS/Journal of Software/Nature Communications. Three paper has been selected as Paper Digest Most Influential Papers (KnowPrompt'WWW22、DocuNet'IJCAI21、AliCG'KDD21). He has served as a PC for NeurIPS/ICLR/ICML/KDD/AAAI/IJCAI/ACL/EMNLP/NAACL, and reviewer of TKDE/WWWJ/JWS/TALLIP/IEEE Transactions on Cybernetics/ESWA.

Tao Gui is an associate professor in Institute of Modern Languages and Linguistics of Fudan University. He is the key member of FudanNLP group. He is a member of ACL, a member of the Youth Working Committee of the Chinese Information Processing Society of China, the member of the Language and Knowledge Computing Professional Committee of the Chinese Information Processing Society of China. He has published more than 30 papers in top international academic conferences and journals such as ACL, ENNLP, AAAI, IJCAI, SIGIR, and so on. He has served as Editor-in-Chief of the NLPR Information Extraction Special Issue, PCs for SIGIR, AAAI, IJCAI, and reviewer for TPAMI and ARR. He has received the Outstanding Doctoral Dissertation Award of the Chinese Information Processing Society of China, the area chair favorite Award of COLING 2018, the outstanding Paper Award of NLPCC 2019, and scholar of young talent promoting project of CAST.

Guoshun Nan is a tenure-track professor in School of Cyber Science and Engineering, Beijing University of Posts and Telecommunications (BUPT). He is the key member of National Engineering Research Center of Mobile Network Security, and a member of Wireless Technology Innovation Institute of BUPT. Before starting academic career, he also worked in Hewlett-Packard Company (China) for more than 4 years as an engineer. He is a member of ACL. His has broad interest in information extraction, model robustness, multimodal retrieval, cyber security and the next generation wireless networks. He has published more than 10 papers in top-tier conferences such as ACL, CVPR, EMNLP, SIGIR, IJCAI, CKIM and Sigcomm. He served as a reviewer for ACL, EMNLP, AAAI, IJCAI, Neurocomputing and IEEE Transaction on Image Processing.