Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans

• URL: http://arxiv.org/abs/2602.14100v1
• Date: Sun, 15 Feb 2026 11:22:12 GMT
• Title: Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans
• Authors: Akhilesh Kakolu Ramarao, Kevin Tang, Dinah Baer-Henney,
• Abstract summary: We show that encoder-decoder models can acquire irregular patterns, but evidence that they generalize these patterns like humans is mixed.<n>We investigate this using the Spanish emphL-shaped morphome, where only the first-person singular indicative shares its stem with all subjunctive forms.<n>None of the models reproduce the human pattern, highlighting the gap between statistical pattern reproduction and morphological abstraction.
• Score: 8.033684021402165
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Whether neural networks can serve as cognitive models of morphological learning remains an open question. Recent work has shown that encoder-decoder models can acquire irregular patterns, but evidence that they generalize these patterns like humans is mixed. We investigate this using the Spanish \emph{L-shaped morphome}, where only the first-person singular indicative (e.g., \textit{pongo} `I put') shares its stem with all subjunctive forms (e.g., \textit{ponga, pongas}) despite lacking apparent phonological, semantic, or syntactic motivation. We compare five encoder-decoder transformers varying along two dimensions: sequential vs. position-invariant positional encoding, and atomic vs. decomposed tag representations. Positional encoding proves decisive: position-invariant models recover the correct L-shaped paradigm clustering even when L-shaped verbs are scarce in training, whereas sequential positional encoding models only partially capture the pattern. Yet none of the models productively generalize this pattern to novel forms. Position-invariant models generalize the L-shaped stem across subjunctive cells but fail to extend it to the first-person singular indicative, producing a mood-based generalization rather than the L-shaped morphomic pattern. Humans do the opposite, generalizing preferentially to the first-person singular indicative over subjunctive forms. None of the models reproduce the human pattern, highlighting the gap between statistical pattern reproduction and morphological abstraction.

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