Xiulin Yang


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Slaapte or Sliep? Extending Neural-Network Simulations of English Past Tense Learning to Dutch and German
Xiulin Yang | Jingyan Chen | Arjan van Eerden | Ahnaf Samin | Arianna Bisazza
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

This work studies the plausibility of sequence-to-sequence neural networks as models of morphological acquisition by humans. We replicate the findings of Kirov and Cotterell (2018) on the well-known challenge of the English past tense and examine their generalizability to two related but morphologically richer languages, namely Dutch and German. Using a new dataset of English/Dutch/German (ir)regular verb forms, we show that the major findings of Kirov and Cotterell (2018) hold for all three languages, including the observation of over-regularization errors and micro U-shape learning trajectories. At the same time, we observe troublesome cases of non human-like errors similar to those reported by recent follow-up studies with different languages or neural architectures. Finally, we study the possibility of switching to orthographic input in the absence of pronunciation information and show this can have a non-negligible impact on the simulation results, with possibly misleading findings.