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Evaluating learning trajectories of neural morphology acquisition models

  • Jordan Kodner EMAIL logo , Salam Khalifa , Sarah Payne and Zoey Liu
Published/Copyright: September 8, 2025
Linguistics Vanguard
From the journal Linguistics Vanguard

Abstract

Computational models of morphology acquisition have played a central role in debates over the nature of morphological representations since the origin of the “past tense debate” in the 1980s. The apparent success of recent artificial neural network architectures for morphological inflection in natural language processing has revitalized this debate. However, despite their often good performance, the actual suitability of these advanced neural networks as models of human morphology acquisition remains uncertain. We argue that much of this confusion stems from inconsistent methods of training and evaluation. In this work, we demonstrate that more careful dataset creation and an evaluation combining quantitative analysis and comparison with human development puts the evaluation of neural models on firmer ground.


Corresponding author: Jordan Kodner, Department of Linguistics & Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA, E-mail:

Funding source: Stony Brook Institute for Advanced Computational Science

Award Identifier / Grant number: Graduate Student Fellowship

Acknowledgments

S.P. gratefully acknowledges funding through the Institute for Advanced Computational Science (IACS) Graduate Research Fellowship and the National Science Foundation (NSF) Graduate Research Fellowship Program under NSF Grant No. 2234683. Some experiments were performed on the SeaWulf HPC cluster maintained by RCC and IACS at Stony Brook University and made possible by NSF Grant No. 1531492. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the IACS or the NSF.

A: Accuracy model

Splittype here refers to sampling strategy. wuetal refers to chr-trm, and kced refers to e-d.

B: Score range and variability models

B.1 Score range

B.2 Score variability

References

Aksu-Koç, Ayhan A. 1985. The acquisition of Turkish. In D. I. Slobin (ed.), The cross-linguistic studies of language acquisition, Vol. 1, The data, 839–876. Hillsdale: Lawrence Erlbaum.Search in Google Scholar

Baayen, R. Harald, Richard Piepenbrock & Leon Gulikers. 1996. The CELEX lexical database (CD-ROM). Philadelphia: Linguistic Data Consortium, University of Pennsylvania.Search in Google Scholar

Batsuren, Khuyagbaatar, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Salehi Ali, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud’hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Cotterell Ryan, Reut Tsarfaty & Ekaterina Vylomova. 2022. UniMorph 4.0: Universal morphology. In Proceedings of the thirteenth language Resources and evaluation conference, 840–855. Marseille: European Language Resources Association. Available at: https://aclanthology.org/2022.lrec-1.89.Search in Google Scholar

Belth, C., S. Payne, D. Beser, J. Kodner & C. Yang. 2021. The greedy and recursive search for morphological productivity. Proceedings of the Annual Meeting of the Cognitive Science Society 43. 2869–2875.Search in Google Scholar

Berko, Jean. 1958. The child’s learning of English morphology. Word 14(2–3). 150–177. https://doi.org/10.1080/00437956.1958.11659661.Search in Google Scholar

Beser, D. 2021. Falling through the gaps: Neural architectures as models of morphological rule learning. Proceedings of the Annual Meeting of the Cognitive Science Society 43. 1042–1048.Search in Google Scholar

Bornstein, Marc H., Linda R. Cote, Sharone Maital, Kathleen Painter, Sung-Yun Park, Liliana Pascual, Marie-Germaine Pêcheux, Josette Ruel, Paola Venuti & Andre Vyt. 2004. Cross-linguistic analysis of vocabulary in young children: Spanish, Dutch, French, Hebrew, Italian, Korean, and American English. Child Development 75(4). 1115–1139. https://doi.org/10.1111/j.1467-8624.2004.00729.x.Search in Google Scholar

Braginsky, Mika, Daniel Yurovsky, Virginia A. Marchman & Michael C. Frank. 2019. Consistency and variability in children’s word learning across languages. Open Mind 3. 52–67. https://doi.org/10.1162/opmi_a_00026.Search in Google Scholar

Breiss, Canaan & Jinyoung Jo. 2023. SIGMORPHON–UniMorph 2023 shared task 0, part 2: Cognitively plausible morphophonological generalization in Korean. In Garrett Nicolai, Eleanor Chodroff, Frederic Mailhot & Çağrı Çöltekin (eds.), Proceedings of the 20th SIGMORPHON Workshop on computational Research in phonetics, phonology, and morphology, 126–131. Toronto: Association for Computational Linguistics.10.18653/v1/2023.sigmorphon-1.14Search in Google Scholar

Brown, Roger. 1973. A first language: The early stages. Cambridge, MA: Harvard University Press.10.4159/harvard.9780674732469Search in Google Scholar

Clahsen, Harald & Monika Rothweiler. 1993. Inflectional rules in children’s grammars: Evidence from German participles. In G. Booij & J. van Marle (eds.), Yearbook of morphology 1992, 1–34. Dordrecht: Springer.10.1007/978-94-017-3710-4_1Search in Google Scholar

Corkery, Maria, Matusevych Yevgen & Sharon Goldwater. 2019. Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection. In Proceedings of the 57th annual Meeting of the Association for computational linguistics, 3868–3877. Florence: Association for Computational Linguistics.10.18653/v1/P19-1376Search in Google Scholar

Cotterell, Ryan, Christo Kirov, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Arya D. McCarthy, Katharina Kann, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, David Yarowsky, Jason Eisner & Mans Hulden. 2018. The CoNLL–SIGMORPHON 2018 shared task: Universal morphological reinflection. In Proceedings of the CoNLL–SIGMORPHON 2018 shared task: Universal morphological reinflection, 1–27. Brussels: Association for Computational Linguistics.Search in Google Scholar

Cotterell, Ryan, Christo Kirov, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia, Manaal Faruqui, Sandra Kübler, David Yarowsky, Jason Eisner & Mans Hulden. 2017. CoNLL–SIGMORPHON 2017 shared task: Universal morphological reinflection in 52 languages. In Proceedings of the CoNLL SIGMORPHON 2017 shared task: Universal morphological reinflection, 1–30. Vancouver: Association for Computational Linguistics. Available at: https://www.aclweb.org/anthology/K17-2001.10.18653/v1/K17-2001Search in Google Scholar

Cotterell, Ryan, Christo Kirov, John Sylak-Glassman, David Yarowsky, Jason Eisner & Mans Hulden. 2016. The SIGMORPHON 2016 shared task: Morphological reinflection. In Proceedings of the 14th SIGMORPHON Workshop on computational research in phonetics, phonology, and morphology, 10–22. Berlin: Association for Computational Linguistics.10.18653/v1/W16-2002Search in Google Scholar

Dankers, Verna, Anna Langedijk, Kate McCurdy, Adina Williams & Dieuwke Hupkes. 2021. Generalising to German plural noun classes, from the perspective of a recurrent neural network. In Proceedings of the 25th conference on computational natural language learning, 94–108. Association for Computational Linguistics.10.18653/v1/2021.conll-1.8Search in Google Scholar

Dawdy-Hesterberg, Lisa Garnand & Janet Breckenridge Pierrehumbert. 2014. Learnability and generalisation of Arabic broken plural nouns. Language, Cognition and Neuroscience 29(10). 1268–1282. https://doi.org/10.1080/23273798.2014.899377.Search in Google Scholar

Deen, Kamil Ud. 2005. The acquisition of Swahili. Amsterdam: John Benjamins.10.1075/lald.40Search in Google Scholar

Elsen, Hilke. 2002. The acquisition of German plurals. In Morphology 2000: Selected papers from the 9th morphology meeting, Vienna, 25–27 February 2000, 117–127. Amsterdam: John Benjamins.10.1075/cilt.218.10elsSearch in Google Scholar

Fenson, Larry, Philip S. Dale, J. Steven Reznick, Elizabeth Bates, Donna J. Thal, Stephen J. Pethick, Michael Tomasello, Carolyn B. Mervis & Joan Stiles. 1994. Variability in early communicative development. Monographs of the Society for Research in Child Development 59(5). 1–185. https://doi.org/10.2307/1166093.Search in Google Scholar

Gale, Robert C., Alexandra C. Salem, Gerasimos Fergadiotis & Steven Bedrick. 2023. Mixed orthographic/phonemic language modeling: Beyond orthographically restricted transformers (BORT). In Burcu Can, Maximilian Mozes, Samuel Cahyawijaya, Naomi Saphra, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Chen Zhao, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette & Lena Voita (eds.), Proceedings of the 8th workshop on representation learning for NLP (RepL4NLP 2023), 212–225. Toronto: Association for Computational Linguistics.10.18653/v1/2023.repl4nlp-1.18Search in Google Scholar

Gawlitzek-Maiwald, Ira. 1994. How do children cope with variation in the input? The case of German plurals and compounding. In Rosemarie Tracy & Elsa Lattey (eds.), How tolerant is Universal Grammar? Essays on language learnability and language variation, 225–266. Tübingen: Niemeyer.10.1515/9783111634777.225Search in Google Scholar

Goldman, Omer, Khuyagbaatar Batsuren, Salam Khalifa, Aryaman Arora, Nicolai Garrett, Reut Tsarfaty & Ekaterina Vylomova. 2023. SIGMORPHON–UniMorph 2023 shared task 0: Typologically diverse morphological inflection. In Garrett Nicolai, Eleanor Chodroff, Frederic Mailhot & Çağrı Çöltekin (eds.), Proceedings of the 20th SIGMORPHON Workshop on computational research in phonetics, phonology, and morphology, 117–125. Toronto: Association for Computational Linguistics.10.18653/v1/2023.sigmorphon-1.13Search in Google Scholar

Goodman, Judith C., Philip S. Dale & Ping Li. 2008. Does frequency count? Parental input and the acquisition of vocabulary. Journal of Child Language 35(3). 515–531. https://doi.org/10.1017/s0305000907008641.Search in Google Scholar

Gorman, Kyle & Steven Bedrick. 2019. We need to talk about standard splits. In Anna Korhonen, David Traum & Lluís Màrquez (eds.), Proceedings of the 57th annual meeting of the association for computational linguistics, 2786–2791. Florence: Association for Computational Linguistics.10.18653/v1/P19-1267Search in Google Scholar

Hart, Betty & Todd R. Risley. 2003. The early catastrophe: The 30 million word gap by age 3. American Educator 27(1). 4–9.Search in Google Scholar

Kirov, Christo & Ryan Cotterell. 2018. Recurrent neural networks in linguistic theory: Revisiting Pinker and Prince (1988) and the past tense debate. Transactions of the Association for Computational Linguistics 6. 651–665. https://doi.org/10.1162/tacl_a_00247.Search in Google Scholar

Kodner, Jordan & S. Khalifa. 2022. SIGMORPHON–UniMorph 2022 shared task 0: Modeling inflection in language acquisition. In Proceedings of the 19th SIGMORPHON Workshop on computational research in phonetics, phonology, and morphology, 157–175. Seattle, WA: Association for Computational Linguistics. Available at: https://aclanthology.org/2022.sigmorphon-1.18.10.18653/v1/2022.sigmorphon-1.18Search in Google Scholar

Kodner, Jordan, S. Khalifa, Khuyagbaatar Batsuren, Hossep Dolatian, Ryan Cotterell, Faruk Akkus, Antonios Anastasopoulos, Taras Andrushko, Aryaman Arora, Nona Atanalov, Gábor Bella, Elena Budianskaya, Yustinus Ghanggo Ate, Omer Goldman, David Guriel, Guriel Simon, Silvia Guriel-Agiashvili, Witold Kieraś, Andrew Krizhanovsky, Natalia Krizhanovsky, Igor Marchenko, M. Markowska, Polina Mashkovtseva, Maria Nepomniashchaya, Daria Rodionova, Karina Scheifer, Alexandra Sorova, Anastasia Yemelina, Jeremiah Young & Ekaterina Vylomova. 2022. SIGMORPHON–UniMorph 2022 shared task 0: Generalization and typologically diverse morphological inflection. In Proceedings of the 19th SIGMORPHON Workshop on computational research in phonetics, phonology, and morphology, 176–203. Seattle, WA: Association for Computational Linguistics. Available at: https://aclanthology.org/2022.sigmorphon-1.19.10.18653/v1/2022.sigmorphon-1.19Search in Google Scholar

Kodner, Jordan, Salam Khalifa & Sarah Ruth Brogden Payne. 2023a. Exploring linguistic probes for morphological generalization. In Houda Bouamor, Juan Pino & Kalika Bali (eds.), Proceedings of the 2023 conference on empirical methods in natural language processing, 8933–8941. Singapore: Association for Computational Linguistics.10.18653/v1/2023.emnlp-main.552Search in Google Scholar

Kodner, Jordan, Sarah Payne, Salam Khalifa & Zoey Liu. 2023b. Morphological inflection: A reality check. In Anna Rogers, Jordan Boyd-Graber & Naoaki Okazaki (eds.), Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), 6082–6101. Toronto: Association for Computational Linguistics.10.18653/v1/2023.acl-long.335Search in Google Scholar

Labov, William. 1972. Some principles of linguistic methodology. Language in Society 1(1). 97–120. https://doi.org/10.1017/s0047404500006576.Search in Google Scholar

Liu, Zoey & Emily Prud’hommeaux. 2022. Data-driven model generalizability in crosslinguistic low-resource morphological segmentation. Transactions of the Association for Computational Linguistics 10. 393–413. https://doi.org/10.1162/tacl_a_00467.Search in Google Scholar

Maamouri, Mohamed, Ann Bies, Tim Buckwalter & Wigdan Mekki. 2004. The Penn Arabic Treebank: Building a large-scale annotated Arabic corpus. In Mahtab Nikkhou (ed.), NEMLAR Conference on Arabic language resources and tools, Vol. 27, 466–467. Cairo: ELDA.Search in Google Scholar

MacWhinney, Brian. 2000. The CHILDES Project, Vol. 2, The database. Abingdon-on-Thames: Psychology Press.Search in Google Scholar

Makarov, Peter & Simon Clematide. 2018. Imitation learning for neural morphological string transduction. In Ellen Riloff, David Chiang, Julia Hockenmaier & Jun’ ichi Tsujii (eds.), Proceedings of the 2018 conference on empirical methods in natural language processing, 2877–2882. Brussels: Association for Computational Linguistics.10.18653/v1/D18-1314Search in Google Scholar

Marcus, Gary F., Ursula Brinkmann, Harald Clahsen, Richard Wiese & Steven Pinker. 1995. German inflection: The exception that proves the rule. Cognitive Psychology 29(3). 189–256. https://doi.org/10.1006/cogp.1995.1015.Search in Google Scholar

Marcus, Gary F., Steven Pinker, Michael Ullman, Michelle Hollander, T. John Rosen, Fei Xu & Harald Clahsen. 1992. Overregularization in language acquisition. Monographs of the Society for Research in Child Development 57(4). 1–178. https://doi.org/10.2307/1166115.Search in Google Scholar

Maslen, Robert, Anna L. Theakston, Elena V. M. Lieven & Michael Tomasello. 2004. A dense corpus study of past tense and plural overregularization in English. Journal of Speech, Language and Hearing Research 47(6). 1319–1333. https://doi.org/10.1044/1092-4388(2004/099).Search in Google Scholar

McCarthy, Arya D., Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia, Kyle Gorman, Ekaterina Vylomova, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, Timofey Arkhangelskiy, Nataly Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ernštreits, Yuval Pinter, Cassandra L. Jacobs, Ryan Cotterell, Mans Hulden & David Yarowsky. 2020. UniMorph 3.0: Universal morphology. In Proceedings of the 12th language resources and evaluation conference, 3922–3931. Marseille: European Language Resources Association. Available at: https://aclanthology.org/2020.lrec-1.483.Search in Google Scholar

McCarthy, John J. & Alan S. Prince. 1990. Foot and word in prosodic morphology: The Arabic broken plural. Natural Language & Linguistic Theory 8. 209–283. https://doi.org/10.1007/bf00208524.Search in Google Scholar

McCarthy, Arya D., Ekaterina Vylomova, Shijie Wu, Chaitanya Malaviya, Lawrence Wolf-Sonkin, Garrett Nicolai, Christo Kirov, Miikka Silfverberg, Sebastian J. Mielke, Jeffrey Heinz, et al.. 2019. The SIGMORPHON 2019 shared task: Morphological analysis in context and cross-lingual transfer for inflection. In Proceedings of the 16th workshop on computational research in phonetics, phonology, and morphology, 229–244. Florence: Association for Computational Linguistics.10.18653/v1/W19-4226Search in Google Scholar

McClelland, James L. & Karalyn Patterson. 2002. Rules or connections in past-tense inflections: What does the evidence rule out? Trends in Cognitive Sciences 6(11). 465–472. https://doi.org/10.1016/s1364-6613(02)01993-9.Search in Google Scholar

McCurdy, Kate, Sharon Goldwater & Adam Lopez. 2020. Inflecting when there’s no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals. In Proceedings of the 58th annual meeting of the association for computational linguistics, 1745–1756. Association for Computational Linguistics.10.18653/v1/2020.acl-main.159Search in Google Scholar

Pimentel, Tiago, Maria Ryskina, Sabrina J. Mielke, Shijie Wu, Eleanor Chodroff, Brian Leonard, Garrett Nicolai, Yustinus Ghanggo Ate, Salam Khalifa, Nizar Habash, Charbel El-Khaissi, Omer Goldman, Michael Gasser, William Lane, Matt Coler, Arturo Oncevay, Jaime Rafael Montoya Samame, Gema Celeste Silva Villegas, Adam Ek, Jean-Philippe Bernardy, Andrey Shcherbakov, Aziyana Bayyr-ool, Karina Sheifer, Sofya Ganieva, Matvey Plugaryov, Elena Klyachko, Salehi Ali, Andrew Krizhanovsky, Natalia Krizhanovsky, Clara Vania, Sardana Ivanova, Aelita Salchak, Christopher Straughn, Zoey Liu, Jonathan North Washington, Duygu Ataman, Witold Kieraś, Marcin Woliński, Totok Suhardijanto, Niklas Stoehr, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Richard J. Hatcher, Emily Prud’hommeaux, Ritesh Kumar, Mans Hulden, Botond Barta, Dorina Lakatos, Gábor Szolnok, Judit Ács, Mohit Raj, David Yarowsky, Cotterell Ryan, Ambridge Ben & Ekaterina Vylomova. 2021. SIGMORPHON 2021 shared task on morphological reinflection: Generalization across languages. In Proceedings of the 18th SIGMORPHON Workshop on computational research in phonetics, phonology, and morphology, 229–259. Association for Computational Linguistics.10.18653/v1/2021.sigmorphon-1.25Search in Google Scholar

Pinker, Steven & Alan Prince. 1988. On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition 28(1–2). 73–193. https://doi.org/10.1016/0010-0277(88)90032-7.Search in Google Scholar

Pinker, Steven & Michael T. Ullman. 2002. The past and future of the past tense. Trends in Cognitive Sciences 6(11). 456–463. https://doi.org/10.1016/s1364-6613(02)01990-3.Search in Google Scholar

Prasada, Sandeep & Steven Pinker. 1993. Generalisation of regular and irregular morphological patterns. Language and Cognitive Processes 8(1). 1–56. https://doi.org/10.1080/01690969308406948.Search in Google Scholar

Ravid, Dorit & Rola Farah. 1999. Learning about noun plurals in early Palestinian Arabic. First Language 19(56). 187–206. https://doi.org/10.1177/014272379901905603.Search in Google Scholar

Rumelhart, David E. & James L. McClelland. 1986. On learning the past tenses of English verbs. Cambridge, MA: MIT Press.Search in Google Scholar

Seidenberg, Mark S. & D. Plaut. 2014. Quasiregularity and its discontents: The legacy of the past tense debate. Cognitive Science 38(6). 1190–1228. https://doi.org/10.1111/cogs.12147.Search in Google Scholar

Swingley, Daniel & Colman Humphrey. 2018. Quantitative linguistic predictors of infants’ learning of specific English words. Child Development 89(4). 1247–1267. https://doi.org/10.1111/cdev.12731.Search in Google Scholar

Szagun, Gisela, Claudia Steinbrink, Melanie Franik & Barbara Stumper. 2006. Development of vocabulary and grammar in young German-speaking children assessed with a German language development inventory. First Language 26(3). 259–280. https://doi.org/10.1177/0142723706056475.Search in Google Scholar

Vylomova, Ekaterina, Jennifer White, Elizabeth Salesky, Sabrina J. Mielke, Shijie Wu, Edoardo Maria Ponti, Rowan Hall Maudslay, Ran Zmigrod, Josef Valvoda, Svetlana Toldova, Francis Tyers, Elena Klyachko, Ilya Yegorov, Natalia Krizhanovsky, Paula Czarnowska, Irene Nikkarinen, Andrew Krizhanovsky, Tiago Pimentel, Lucas Torroba Hennigen, Christo Kirov, Nicolai Garrett, Adina Williams, Antonios Anastasopoulos, Hilaria Cruz, Eleanor Chodroff, Cotterell Ryan, Miikka Silfverberg & Mans Hulden. 2020. SIGMORPHON 2020 shared task 0: Typologically diverse morphological inflection. In Proceedings of the 17th SIGMORPHON Workshop on computational research in phonetics, phonology, and morphology, 1–39. Association for Computational Linguistics.10.18653/v1/2020.sigmorphon-1.1Search in Google Scholar

Wehrli, Silvan, Clematide Simon & Peter Makarov. 2022. CLUZH at SIGMORPHON 2022 shared tasks on morpheme segmentation and inflection generation. In Proceedings of the 19th SIGMORPHON Workshop on computational research in phonetics, phonology, and morphology, 212–219. Seattle, WA: Association for Computational Linguistics.10.18653/v1/2022.sigmorphon-1.21Search in Google Scholar

Wiemerslage, Adam, Shiran Dudy & Katharina Kann. 2022. A comprehensive comparison of neural networks as cognitive models of inflection. In Proceedings of the 2022 conference on empirical methods in natural language processing, 1933–1945. Abu Dhabi: Association for Computational Linguistics. Available at: https://aclanthology.org/2022.emnlp-main.126.10.18653/v1/2022.emnlp-main.126Search in Google Scholar

Wu, Shijie & Ryan Cotterell. 2019. Exact hard monotonic attention for character-level transduction. In Anna Korhonen, David Traum & Lluís Màrquez (eds.), Proceedings of the 57th annual meeting of the association for computational linguistics, 1530–1537. Florence: Association for Computational Linguistics.10.18653/v1/P19-1148Search in Google Scholar

Wu, Shijie, Ryan Cotterell & Timothy O’Donnell. 2019. Morphological irregularity correlates with frequency. In Proceedings of the 57th annual meeting of the association for computational linguistics, 5117–5126. Florence: Association for Computational Linguistics.10.18653/v1/P19-1505Search in Google Scholar

Xu, Fei & Steven Pinker. 1995. Weird past tense forms. Journal of Child Language 22(3). 531–556. https://doi.org/10.1017/s0305000900009946.Search in Google Scholar

Received: 2024-11-01
Accepted: 2025-05-20
Published Online: 2025-09-08

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