Joe Pater


2022

pdf bib
Learning Stress Patterns with a Sequence-to-Sequence Neural Network
Brandon Prickett | Joe Pater
Proceedings of the Society for Computation in Linguistics 2022

2020

pdf bib
Proceedings of the Society for Computation in Linguistics 2020
Allyson Ettinger | Gaja Jarosz | Joe Pater
Proceedings of the Society for Computation in Linguistics 2020

2019

pdf bib
Proceedings of the Society for Computation in Linguistics (SCiL) 2019
Gaja Jarosz | Max Nelson | Brendan O’Connor | Joe Pater
Proceedings of the Society for Computation in Linguistics (SCiL) 2019

2018

pdf bib
Proceedings of the Society for Computation in Linguistics (SCiL) 2018
Gaja Jarosz | Brendan O’Connor | Joe Pater
Proceedings of the Society for Computation in Linguistics (SCiL) 2018

pdf bib
Seq2Seq Models with Dropout can Learn Generalizable Reduplication
Brandon Prickett | Aaron Traylor | Joe Pater
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

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.

2015

pdf bib
Sign constraints on feature weights improve a joint model of word segmentation and phonology
Mark Johnson | Joe Pater | Robert Staubs | Emmanuel Dupoux
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

pdf bib
Learning probabilities over underlying representations
Joe Pater | Robert Staubs | Karen Jesney | Brian Smith
Proceedings of the Twelfth Meeting of the Special Interest Group on Computational Morphology and Phonology