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Article

Dynamic pooling and unfolding recursive autoencoders for paraphrase detection

Authors:

Richard Socher,

Jeffrey Pennington,

Christopher D. ManningAuthors Info & Claims

NIPS'11: Proceedings of the 25th International Conference on Neural Information Processing Systems

Pages 801 - 809

Published: 12 December 2011 Publication History

Abstract

Paraphrase detection is the task of examining two sentences and determining whether they have the same meaning. In order to obtain high accuracy on this task, thorough syntactic and semantic analysis of the two statements is needed. We introduce a method for paraphrase detection based on recursive autoencoders (RAE). Our unsupervised RAEs are based on a novel unfolding objective and learn feature vectors for phrases in syntactic trees. These features are used to measure the word- and phrase-wise similarity between two sentences. Since sentences may be of arbitrary length, the resulting matrix of similarity measures is of variable size. We introduce a novel dynamic pooling layer which computes a fixed-sized representation from the variable-sized matrices. The pooled representation is then used as input to a classifier. Our method outperforms other state-of-the-art approaches on the challenging MSRP paraphrase corpus.

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Cited By

Samandi VTiňo PBahsoon R(2023)Real-Time Workflow Scheduling in Cloud with Recursive Neural Network and List SchedulingHybrid Artificial Intelligent Systems10.1007/978-3-031-40725-3_21(244-255)Online publication date: 5-Sep-2023
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Dynamic pooling and unfolding recursive autoencoders for paraphrase detection

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Information & Contributors

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Published In

cover image Guide Proceedings

NIPS'11: Proceedings of the 25th International Conference on Neural Information Processing Systems

December 2011

2752 pages

ISBN:9781618395993

Publisher

Curran Associates Inc.

Red Hook, NY, United States

Publication History

Published: 12 December 2011

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Cited By

Samandi VTiňo PBahsoon R(2023)Real-Time Workflow Scheduling in Cloud with Recursive Neural Network and List SchedulingHybrid Artificial Intelligent Systems10.1007/978-3-031-40725-3_21(244-255)Online publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-40725-3_21
Ebrahimi FTushev MMahmoud A(2021)Classifying Mobile Applications Using Word EmbeddingsACM Transactions on Software Engineering and Methodology10.1145/347482731:2(1-30)Online publication date: 17-Nov-2021
https://dl.acm.org/doi/10.1145/3474827
Shao JWang YGao HShen HLi YCheng XDemartini GZuccon GCulpepper JHuang ZTong H(2021)Locate Who You AreProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482134(3413-3417)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482134
Zhou QHui TWang RHu HLiu S(2021)Attentive Excitation and Aggregation for Bilingual Referring Image SegmentationACM Transactions on Intelligent Systems and Technology10.1145/344634512:2(1-17)Online publication date: 26-Feb-2021
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Kumar SRoy SPathak VVarma VKambhampati SBhattacharya ANatarajan SRoy R(2020)A Hybrid Distributed Model for Learning Representation of Short Texts with Attribute LabelsProceedings of the 7th ACM IKDD CoDS and 25th COMAD10.1145/3371158.3371195(244-248)Online publication date: 5-Jan-2020
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Azaria ANivasch KElkind EVeloso MAgmon NTaylor M(2019)The Multimodal Correction Detection ProblemProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331918(1784-1786)Online publication date: 8-May-2019
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