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Frequency matters: Modeling irregular morphological patterns in Spanish with Transformers

2024/10/28 by Akhilesh Kakolu Ramarao, Kevin Tang, Ramarao, Akhilesh Kakolu +3 · 1 citation
Computer Science · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Phonetics and Phonology Research #Speech and dialogue systems

paper · pdf · doi:10.48550/arxiv.2410.21013

openalex publication_date 2024/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Over the past decade, various studies have addressed how speakers solve the so-called `The Paradigm Cell Filling Problem' (PCFP) \citepackerman2009parts across different languages. The PCFP addresses a fundamental question in morphological processing: how do speakers accurately generate inflected forms of words when presented with incomplete paradigms? This problem is particularly salient when modeling complex inflectional systems. We focus on Spanish verbal paradigms, where certain verbs follow an irregular L-shaped pattern, where the first-person singular present indicative stem matches the stem used throughout the present subjunctive mood. We formulate the problem as a morphological reinflection task. Specifically, we investigate the role of input frequency in the acquisition of regular versus irregular L-shaped patterns in transformer models. By systematically manipulating the input distributions and analyzing model behavior, we reveal four key findings: 1) Models perform better on L-shaped verbs compared to regular verbs, especially in uneven frequency conditions; 2) Robust primacy effects are observed, but no consistent recency effects; 3) Memorization becomes more prominent as the proportion of L-shaped verbs increases; 4) There is a tendency to regularize L-shaped verbs when their consonant alternation pairs are rare or absent in the training data.

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