@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)
ACL