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  1. ; (, Proceedings of the Society for Computation in Linguistics)
    Ettinger, Allyson; Hunter, Tim; Prickett, Brandon (Ed.)
    We present the first application of modern neural networks to the well studied task of learning word stress systems. We tested our adaptation of a sequence-to-sequence network on the Tesar and Smolensky test set of 124 “languages”, showing that it acquires generalizable representations of stress patterns in a very high proportion of runs. We also show that it learns restricted lexically conditioned patterns, known as stress windows. The ability of this model to acquire lexical idiosyncracies, which are very common in natural language systems, sets it apart from past, non-neural models tested on the Tesar and Smolensky data set. 
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  2. ; (, Proceedings of the Society for Computation in Linguistics)
    Ettinger, Allyson; Pavlich, Ellie; Prickett, Brandon (Ed.)
    Morphological patterns can involve simple concatenation of fixed strings (e.g., unkind, kindness) or ‘nonconcatenative’ processes such as infixation (e.g., Chamorro l-um-iʔeʔ ‘saw (actor-focus)’, Topping, 1973) and reduplication (e.g., Amele ba-bagawen ‘as he came out’, Roberts, 1987), among many others (e.g., Anderson, 1992; Inkelas, 2014). Recent work has established that deep neural networks are capable of inducing both concatenative and nonconatenative patterns (e.g., Kannand Schütze, 2017; Nelson et al., 2020). In this paper, we verify that encoder-decoder networks can learn and generalize attested types of infixation and reduplication from modest training sets. We show further that the same networks readily learn many infixation and reduplication patterns that are unattested in natural languages, raising questions about their relationship to linguistic theory and viability as models of human learning. 
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