Radical Allomorphy: Phonological Surface Forms without Phonology (2025)

Abstract: Recent computational work typically frames morphophonology as generating surface forms (SFs) from abstract underlying representations (URs) by applying phonological rules or constraints. This generative stance presupposes that every morpheme has a well-defined UR from which all allomorphs can be derived, a theory-laden assumption that is expensive to annotate, especially in low-resource settings.We adopt an alternative view. Allomorphs and their phonological variants are treated as the basic, observed lexicon, not as outputs of abstract URs. The modeling task therefore shifts from deriving SFs to selecting the correct SF, given a meaning and a phonological context. This discriminative formulation removes the need to posit or label URs and lets the model exploit the surface evidence directly.

## introduction computational morphophonology has long modeled the problem of deriving spoken forms, also known linguistically as surface forms (sfs), from underlying representations (urs) using some form of transformations. whether within rule-based frameworks, constraint-based theories, or neural sequence-to-sequence approaches, this generative tradition hinges on the idea that urs exist as abstract, latent forms from which observable variants, or allomorphs, can be derived. yet despite the large amount of work invested in this problem, the status of the ur remains conceptually and practically fraught: what constitutes a valid ur? how are its properties discovered, and how does one distinguish it from a mere analytical convenience? is it suitable when observable data is scarce, where there aren't enough examples to hypothesize a ur? in this paper, we explore a radically different approach. rather than positing urs and seeking generative mechanisms to produce sfs, we propose a model in which the full set of surface vari- root +plural sf ur ss sf dog +s {+s, +z, +iz} +z [dogz] cat +s {+s, +z, +iz} +s [kaets] fox +s {+s, +z, +iz} +iz [faksiz] ox +en {+en} +en [qks@n] ants, the combination of the allomorphs and their phonological surface variants (which we will call allomorphones), is taken as primary. the task then becomes one of enumeration and selection: given a context, choose the appropriate sf from a prespecified inventory. this reframing transforms the problem from a generative to a discriminative one, with significant implications for both linguistic theory and computational modeling. figure 1 illustrates how multiple allomorphones of the +plural morpheme for english coexist in the surface set, with one selected per context. by grounding analyses in observable surface data rather than abstract representations, our approach emphasizes empirical adequacy and learnability. it aligns with recent computational trends favoring surface-level, context-sensitive modeling. the approach is particularly exciting in lowresource settings for which a limited amount of data is available, as creating a ur requires theoretical decisions about the phonological system of the language or language variant, while our approach only requires morphological segmentation and part-of-speech annotation. we demonstrate its utility through a case study in egyptian arabic for its complex morphophonology. we compare our approach with existing models analogous to generative approaches. we also show that our model excels in low-resource settings compared to others. 20435 2 background and related work ## our approach: radical allomorphy in this approach, we shift the focus from the direct relationship between the actual form of the underlying representation to what it represents: a set of realized surface forms. we explore an approach analogous to mat, where we don't assume a phonological ur and instead represent the different realizations of morphs as a set of possible sfs. by this definition, the task becomes one of enumeration and selection rather than an explicit mapping between a form and its realization. we define three key notions: this approach, naturally, assumes the data to be a parallel set of segmented sfs with their corresponding mtags and possibly hmorphs. this allows the extraction and clustering of the alm sets and linking them to the different levels of representations. we illustrate this terminology in ## conclusion and outlook in this work, we empirically explored a novel approach to the task of modeling morphophonology. instead of positing underlying representations or learning mappings from a rigid abstract representation to generate a spoken form, we proposed a rather discriminative approach to select the correct surface form from an enumerated set of candidate surface forms. we find that the most radical allomorphy approach that employs morphological tags as identities of the morphs is more restrictive than a purely form-based allomorphy approach. we also show that our best setup outperforms a rule-inducing sota using around 25% of the data only. additionally, our approach outperforms the sota character-based transformer in the same task in the low-resource scenario. from a theoretical perspective, adopting a generative phonology or a morpheme alternate theory based approach will have empirical implications on the quality of the resulting spoken forms and the representation of the acquired grammar, as our work shows. this sets the path for further exploration on modeling such theories computationally and what it means for practical applications. from a practical nlp point of view, the main takeway is that our approach generates spoken forms of words efficiently at low-resource settings and acquires an interpretable representation. we plan to explore additional starting points and selection criteria in addition to adding a learning component that extrapolates allomorphones in a way inspired by computational models of language acquisition. we also plan to evaluate this approach on different dialects of arabic as standalone varieties or cross-dialectal evaluation.