Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
Paper summary #### Problem addressed: Paraphrase Detection using Recursive Auto Encoders #### Summary: The authors present the usage of autoencoders to learn recursive tree (in a semantic sense) structures in data. They use deep recursive autoencoders to learn a representation for natural language sentences in unsupervised and semi-supervised frameworks. They then introduce a pooling scheme on top of this representation to handle sentences of varying length and determine if they are paraphrases of each other and achieve state of the art results on MSRP paraphrase corpus. #### Novelty: The idea of unfolding recursive autoencoders (RAE), Pooling to handle sentences and representations of varying sentence lengths. #### Datasets: MSRP paraphrase corpus #### Additional remarks: The main idea was introduced in Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions, R. Socher, J. Pennington, E. H. Huang, A. Y. Ng, and C. D. Manning. In EMNLP, 2011. Please refer that for training of RAEs. #### Resources: paper: http://www.socher.org/uploads/Main/SocherHuangPenningtonNgManning_NIPS2011.pdf #### Presenter: Bhargava U. Kota
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Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
Socher, Richard and Huang, Eric H. and Pennington, Jeffrey and Ng, Andrew Y. and Manning, Christopher D.
Neural Information Processing Systems Conference - 2011 via Local Bibsonomy
Keywords: dblp


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