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# 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](https://md.mova.elamur.ai/free/en/docs/papers/corkery-2019-are-we-there-yet-encoder/)

1. [How much capacity does Turkish inflection require? An empirical study of GRU encoder–decoder bottlenecks.](https://aclanthology.org/2026.scil-main.39/) *— 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](https://www.anlp.jp/proceedings/annual_meeting/2024/#E9-1) *— 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](https://md.mova.elamur.ai/free/en/docs/papers/ma-2022-how-do-we-get-there/) *— 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](https://arxiv.org/abs/2210.03050) *— 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](https://aclanthology.org/2021.conll-1.8/) *— 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](https://aclanthology.org/2021.sigmorphon-1.28/) *— 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](https://aclanthology.org/2020.cmcl-1.8/) *— 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](https://aclanthology.org/2020.acl-main.159/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
9. [The Paradigm Discovery Problem](https://aclanthology.org/2020.acl-main.695/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
10. [Weird Inflects but OK: Making Sense of Morphological Generation Errors](https://aclanthology.org/K19-1014/) *— 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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