@InProceedings{jiang-EtAl:2017:EMNLP2017,
  author    = {Jiang, Zhuoxuan  and  Feng, Shanshan  and  Cong, Gao  and  Miao, Chunyan  and  Li, Xiaoming},
  title     = {A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2768--2773},
  abstract  = {Recent years have witnessed the proliferation of Massive Open Online Courses
	(MOOCs). With massive learners being offered MOOCs, there is a demand that the
	forum contents within MOOCs need to be classified in order to facilitate both
	learners and instructors. Therefore we investigate a significant application,
	which is to associate forum threads to subtitles of video clips. This task can
	be regarded as a document ranking problem, and the key is how to learn a
	distinguishable text representation from word sequences and learners' behavior
	sequences. In this paper, we propose a novel cascade model, which can capture
	both the latent semantics and latent similarity by modeling MOOC data.
	Experimental results on two real-world datasets demonstrate that our textual
	representation outperforms state-of-the-art unsupervised counterparts for the
	application.},
  url       = {https://www.aclweb.org/anthology/D17-1293}
}