Automatic Knowledge Fusion in Transferrable Networks for Semantic Text Matching

  • Cen Chen
  • , Ya Lin Zhang
  • , Minghui Qiu
  • , Bingzhe Wu
  • , Li Wang
  • , Longfei Li
  • , Jun Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Transfer learning has been widely studied in many real-world applications to help the model performance of data-deficient domains. However, the design choice, such as what, where and how to share, can greatly influence the model performance. To save the effort in the transfer learning model design, we propose a novel method to automatically fuse transferable network architecture. Extensive experiments on public datasets of semantic text matching tasks show that our proposed method has better performance than the state-of-the-art transfer learning architectures.

Original languageEnglish
Title of host publicationThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery
Pages73-74
Number of pages2
ISBN (Electronic)9781450370240
DOIs
StatePublished - 20 Apr 2020
Externally publishedYes
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020

Publication series

NameThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China
CityTaipei
Period20/04/2024/04/20

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