Papers with Kirov
Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals (2020.acl-main)
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| Challenge: | Encoder-decoder models can be used to generalize to inflectional morphology and generalize new words, but they fail on tasks like German number inflection, where infrequent suffixes like /-s/ can still be productively generalized. |
| Approach: | They propose to use a dataset to collect data from German speakers to examine whether ED models can generalize the most frequently produced plural class. |
| Outcome: | The proposed model does not show human-like variability or ‘regular’ extension of other plural markers. |
Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection (P19-1)
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| Challenge: | linguistics and cognitive science have long debated the cognitive mechanisms needed to account for the English past tense. |
| Approach: | They propose to use an encoder-decoder model to account for the english past tense . they also show that ED models demonstrate humanlike performance in a nonce-word task . |
| Outcome: | The proposed model is unstable across simulations and does not fit to human data . other neural models might do better, but there is insufficient evidence to claim them . |