Who cites «Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection»

20 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

1. How much capacity does Turkish inflection require? An empirical study of GRU encoder–decoder bottlenecks. — 2026 · Proceedings of the Society for Computation in Linguistics 2026
2. 小規模言語モデルによる子供の過剰一般化のモデリング — 2024 · 言語処理学会 第30回年次大会 発表論文集 (NLP2024)
3. How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection — our card · 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
4. State-of-the-art generalisation research in NLP: A taxonomy and review — 2022 · arXiv:2210.03050 · Nat Mach Intell 5, 1161-1174 (2023)
5. Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network — 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning
6. Not Quite There Yet: Combining Analogical Patterns and Encoder-Decoder Networks for Cognitively Plausible Inflection — 2021 · Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
7. Conditioning, but on Which Distribution? Grammatical Gender in German Plural Inflection — 2020 · Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
8. Inflecting When There''s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
9. The Paradigm Discovery Problem — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
10. Weird Inflects but OK: Making Sense of Morphological Generation Errors — 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
11. beser-2021-falling-through-gaps-neural-architectures
12. fukatsu-2024-learning-bidirectional-morphological-inflection-like
13. haga-2024-modeling-overregularization-children-small-language
14. moisio-2024-llms-morphological-analyses-complex-fst
15. payne-2025-lemmas-matter-but-not-like
16. prickett-2022-learning-reduplication-neural-network-that
17. prickett-2022-learning-stress-patterns-sequence-sequence
18. prickett-2025-probing-neural-network-generalization-default
19. rosen-2022-modeling-human-like-morphological-prediction
20. wiemerslage-2022-comprehensive-comparison-neural-networks-as

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