Chenyu Wang


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MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs
Jifan Yu | Gan Luo | Tong Xiao | Qingyang Zhong | Yuquan Wang | Wenzheng Feng | Junyi Luo | Chenyu Wang | Lei Hou | Juanzi Li | Zhiyuan Liu | Jie Tang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e.g., course concept extraction, prerequisite relation discovery, etc. However, the publicly available datasets of MOOC are limited in size with few types of data, which hinders advanced models and novel attempts in related topics. Therefore, we present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. Moreover, we conduct a prerequisite discovery task as an example application to show the potential of MOOCCube in facilitating relevant research. The data repository is now available at

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Financial News Annotation by Weakly-Supervised Hierarchical Multi-label Learning
Hang Jiang | Zhongchen Miao | Yuefeng Lin | Chenyu Wang | Mengjun Ni | Jian Gao | Jidong Lu | Guangwei Shi
Proceedings of the Second Workshop on Financial Technology and Natural Language Processing


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Course Concept Expansion in MOOCs with External Knowledge and Interactive Game
Jifan Yu | Chenyu Wang | Gan Luo | Lei Hou | Juanzi Li | Zhiyuan Liu | Jie Tang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drifts and lack of knowledge guidance, existing methods can not effectively expand course concepts in complex MOOC environments. In this paper, we first build a novel boundary during searching for new concepts via external knowledge base and then utilize heterogeneous features to verify the high-quality results. In addition, to involve human efforts in our model, we design an interactive optimization mechanism based on a game. Our experiments on the four datasets from Coursera and XuetangX show that the proposed method achieves significant improvements(+0.19 by MAP) over existing methods.