@inproceedings{jiang-etal-2017-novel,
title = "A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of {MOOC}",
author = "Jiang, Zhuoxuan and
Feng, Shanshan and
Cong, Gao and
Miao, Chunyan and
Li, Xiaoming",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1293/",
doi = "10.18653/v1/D17-1293",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
%A Jiang, Zhuoxuan
%A Feng, Shanshan
%A Cong, Gao
%A Miao, Chunyan
%A Li, Xiaoming
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F jiang-etal-2017-novel
%X 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.
%R 10.18653/v1/D17-1293
%U https://aclanthology.org/D17-1293/
%U https://doi.org/10.18653/v1/D17-1293
%P 2768-2773
Markdown (Informal)
[A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC](https://aclanthology.org/D17-1293/) (Jiang et al., EMNLP 2017)
- A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC (Jiang et al., EMNLP 2017)
ACL
- Zhuoxuan Jiang, Shanshan Feng, Gao Cong, Chunyan Miao, and Xiaoming Li. 2017. A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2768–2773, Copenhagen, Denmark. Association for Computational Linguistics.