Adaptive convolution for text classification

Byung Ju Choi, Jun Hyung Park, Sang Keun Lee

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

Abstract

In this paper, we present an adaptive convolution for text classification to give stronger flexibility to convolutional neural networks (CNNs). Unlike traditional convolutions that use the same set of filters regardless of different inputs, the adaptive convolution employs adaptively generated convolutional filters that are conditioned on inputs. We achieve this by attaching filter-generating networks, which are carefully designed to generate input-specific filters, to convolution blocks in existing CNNs. We show the efficacy of our approach in existing CNNs based on our performance evaluation. Our evaluation indicates that adaptive convolutions improve all the baselines, without any exception, as much as up to 2.6 percentage point in seven benchmark text classification datasets.

Original languageEnglish
Title of host publicationLong and Short Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages2475-2485
Number of pages11
ISBN (Electronic)9781950737130
Publication statusPublished - 2019
Event2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States
Duration: 2019 Jun 22019 Jun 7

Publication series

NameNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume1

Conference

Conference2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
CountryUnited States
CityMinneapolis
Period19/6/219/6/7

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Adaptive convolution for text classification'. Together they form a unique fingerprint.

  • Cite this

    Choi, B. J., Park, J. H., & Lee, S. K. (2019). Adaptive convolution for text classification. In Long and Short Papers (pp. 2475-2485). (NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference; Vol. 1). Association for Computational Linguistics (ACL).