@inproceedings{alderete-etal-2026-paradigms,
title = "Paradigms over Surface Cues: Inflection Class Drives {H}indi Gender Assignment in Expert-Annotated Noun Paradigms",
author = "Alderete, John and
Agrawal, Yogya and
Nguyen, Tra My and
Shah, Puja and
Mohan, Aanchan",
editor = "Adelani, David Ifeoluwa and
Alabi, Jesujoba and
Arnett, Catherine and
Chang, Tyler and
Downey, C.M. and
Goldman, Omer and
Li, Senyu and
Miranda, Lester James Validad and
Qi, Jirui and
Stap, David and
Winata, Genta Indra and
Zhang, Xinyu Crystina",
booktitle = "Proceedings of the 6th Workshop on Multilingual Representation Learning ({MRL} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mrl-1.9/",
pages = "102--118",
ISBN = "979-8-89176-469-9",
abstract = "Grammatical gender assignment is governed by interacting phonological, semantic, and morphological factors whose relative weight varies across languages. We present the first large-scale computational analysis of gender in Hindi using a novel dataset of over 6,000 noun paradigms annotated for morphological, phonological, semantic, and lexical strata features. We tested inflection class against competing predictors with interpretable classifiers and neural networks and found that it dominates, predicting gender at {\ensuremath{\approx}}97{\%} accuracy, while other features contribute negligibly. The dependence is asymmetric: inflection class nearly determines gender, whereas gender is only a partial predictor of inflection class. Inflection class is itself recoverable in part from phonological and etymological cues without reference to gender, which suggests a resolution to the bootstrapping problem: form-based cues scaffold inflection class, which in turn scaffolds gender. These findings establish Hindi as an extreme case of morphologically aligned gender and expose a limitation of language-agnostic approaches: methods that treat semantic and surface-form cues as broadly predictive of gender may miss Hindi{'}s near-deterministic dependence on inflection class. Our dataset enables probing whether multilingual models encode this dependency and whether it survives cross-lingual transfer."
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<abstract>Grammatical gender assignment is governed by interacting phonological, semantic, and morphological factors whose relative weight varies across languages. We present the first large-scale computational analysis of gender in Hindi using a novel dataset of over 6,000 noun paradigms annotated for morphological, phonological, semantic, and lexical strata features. We tested inflection class against competing predictors with interpretable classifiers and neural networks and found that it dominates, predicting gender at \ensuremath\approx97% accuracy, while other features contribute negligibly. The dependence is asymmetric: inflection class nearly determines gender, whereas gender is only a partial predictor of inflection class. Inflection class is itself recoverable in part from phonological and etymological cues without reference to gender, which suggests a resolution to the bootstrapping problem: form-based cues scaffold inflection class, which in turn scaffolds gender. These findings establish Hindi as an extreme case of morphologically aligned gender and expose a limitation of language-agnostic approaches: methods that treat semantic and surface-form cues as broadly predictive of gender may miss Hindi’s near-deterministic dependence on inflection class. Our dataset enables probing whether multilingual models encode this dependency and whether it survives cross-lingual transfer.</abstract>
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%0 Conference Proceedings
%T Paradigms over Surface Cues: Inflection Class Drives Hindi Gender Assignment in Expert-Annotated Noun Paradigms
%A Alderete, John
%A Agrawal, Yogya
%A Nguyen, Tra My
%A Shah, Puja
%A Mohan, Aanchan
%Y Adelani, David Ifeoluwa
%Y Alabi, Jesujoba
%Y Arnett, Catherine
%Y Chang, Tyler
%Y Downey, C. M.
%Y Goldman, Omer
%Y Li, Senyu
%Y Miranda, Lester James Validad
%Y Qi, Jirui
%Y Stap, David
%Y Winata, Genta Indra
%Y Zhang, Xinyu Crystina
%S Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-469-9
%F alderete-etal-2026-paradigms
%X Grammatical gender assignment is governed by interacting phonological, semantic, and morphological factors whose relative weight varies across languages. We present the first large-scale computational analysis of gender in Hindi using a novel dataset of over 6,000 noun paradigms annotated for morphological, phonological, semantic, and lexical strata features. We tested inflection class against competing predictors with interpretable classifiers and neural networks and found that it dominates, predicting gender at \ensuremath\approx97% accuracy, while other features contribute negligibly. The dependence is asymmetric: inflection class nearly determines gender, whereas gender is only a partial predictor of inflection class. Inflection class is itself recoverable in part from phonological and etymological cues without reference to gender, which suggests a resolution to the bootstrapping problem: form-based cues scaffold inflection class, which in turn scaffolds gender. These findings establish Hindi as an extreme case of morphologically aligned gender and expose a limitation of language-agnostic approaches: methods that treat semantic and surface-form cues as broadly predictive of gender may miss Hindi’s near-deterministic dependence on inflection class. Our dataset enables probing whether multilingual models encode this dependency and whether it survives cross-lingual transfer.
%U https://aclanthology.org/2026.mrl-1.9/
%P 102-118
Markdown (Informal)
[Paradigms over Surface Cues: Inflection Class Drives Hindi Gender Assignment in Expert-Annotated Noun Paradigms](https://aclanthology.org/2026.mrl-1.9/) (Alderete et al., MRL 2026)
ACL