Convolutional neural network for paraphrase identification

Wenpeng Yin, Hinrich Schütze

Research output: Chapter in Book/Report/Conference proceedingConference contribution

192 Scopus citations

Abstract

We present a new deep learning architecture Bi-CNN-MI for paraphrase identification (PI). Based on the insight that PI requires comparing two sentences on multiple levels of granularity, we learn multigranular sentence representations using convolutional neural network (CNN) and model interaction features at each level. These features are then the input to a logistic classifier for PI. All parameters of the model (for embeddings, convolution and classification) are directly optimized for PI. To address the lack of training data, we pretrain the network in a novel way using a language modeling task. Results on the MSRP corpus surpass that of previous NN competitors.

Original languageEnglish (US)
Title of host publicationNAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages901-911
Number of pages11
ISBN (Electronic)9781941643495
DOIs
StatePublished - 2015
EventConference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015 - Denver, United States
Duration: May 31 2015Jun 5 2015

Publication series

NameNAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Conference

ConferenceConference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015
Country/TerritoryUnited States
CityDenver
Period5/31/156/5/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Language and Linguistics
  • Linguistics and Language

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