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CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks

  • Authors
  • Dessi, R.; Baroni, M.
  • UPF authors
  • BARONI ., MARCO;
  • Authors of the book
  • Nakov, P.; Palmer, A. (eds.)
  • Book title
  • The 57th Annual Meeting of the Association for Computational Linguistics (Proceedings of the Conference)
  • Publisher
  • Association for Computational Linguistics
  • Publication year
  • 2019
  • Pages
  • 3919-3923
  • ISBN
  • 978-1-950737-48-2
  • Abstract
  • Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of ¿jump around¿ 0-shot from the component words. Recurrent networks (RNNs) were found to completely fail the most challenging generalization cases. We test here a convolutional network (CNN) on these tasks, reporting hugely improved performance with respect to RNNs. Despite the big improvement, the CNN has however not induced systematic rules, suggesting that the difference between compositional and non-compositional behaviour is not clear-cut.
  • Complete citation
  • Dessi, R.; Baroni, M.. CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks. In: Nakov, P.; Palmer, A. (eds.). The 57th Annual Meeting of the Association for Computational Linguistics (Proceedings of the Conference). 1 ed. East Stroudsburg PA: Association for Computational Linguistics; 2019. p. 3919-3923.