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Seq2Seq Models with Dropout can Learn Generalizable Reduplication
Proceedings of SIGMORPHON (2018)
  • Brandon Prickett
  • Aaron Traylor, Brown University
  • Joe Pater
Natural language reduplication can pose a challenge to neural models of language, and has been argued to require variables (Marcus et al., 1999). Sequence-to-sequence neural networks have been shown to perform well at a number of other morphological tasks (Cotterell et al., 2016), and produce results that highly correlate with human behavior (Kirov, 2017; Kirov & Cotterell, 2018) but do not include any explicit variables in their architecture. We find that they can learn a reduplicative pattern that generalizes to novel segments if they are trained with dropout (Srivastava et al., 2014). We argue that this matches the scope of generalization observed in human reduplication.
  • Neural networks,
  • connectionism,
  • reduplication
Publication Date
Fall 2018
Citation Information
Brandon Prickett, Aaron Traylor and Joe Pater. "Seq2Seq Models with Dropout can Learn Generalizable Reduplication" Proceedings of SIGMORPHON (2018)
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