Learning inflection classes using Adaptive Resonance Theory

• URL: http://arxiv.org/abs/2512.15551v1
• Date: Wed, 17 Dec 2025 15:58:20 GMT
• Title: Learning inflection classes using Adaptive Resonance Theory
• Authors: Peter Dekker, Heikki Rasilo, Bart de Boer,
• Abstract summary: We study the learnability of a system of verbal inflection classes by the individual language user.<n>We use Adaptive Resonance Theory, a neural network with a parameter that determines the degree of generalisation (vigilance)<n>The similarity of clustering to attested inflection classes varies depending on the complexity of the inflectional system.
• Score: 0.2676349883103403
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The concept of inflection classes is an abstraction used by linguists, and provides a means to describe patterns in languages that give an analogical base for deducing previously unencountered forms. This ability is an important part of morphological acquisition and processing. We study the learnability of a system of verbal inflection classes by the individual language user by performing unsupervised clustering of lexemes into inflection classes. As a cognitively plausible and interpretable computational model, we use Adaptive Resonance Theory, a neural network with a parameter that determines the degree of generalisation (vigilance). The model is applied to Latin, Portuguese and Estonian. The similarity of clustering to attested inflection classes varies depending on the complexity of the inflectional system. We find the best performance in a narrow region of the generalisation parameter. The learned features extracted from the model show similarity with linguistic descriptions of the inflection classes. The proposed model could be used to study change in inflection classes in the future, by including it in an agent-based model.

Related papers

• Analyzing Finnish Inflectional Classes through Discriminative Lexicon and Deep Learning Models [42.045109659898465]
  Inflectional classes bring together nouns which have similar stem changes and use similar exponents in their paradigms.<n>It is unclear whether inflectional classes are cognitively real.<n>This study uses a dataset with 55,271 inflected nouns of 2000 high-frequency Finnish nouns from 49 inflectional classes.
  arXiv  Detail & Related papers  (2025-09-05T05:24:56Z)
• Gumbel Counterfactual Generation From Language Models [64.55296662926919]
  We show that counterfactual reasoning is conceptually distinct from interventions.<n>We propose a framework for generating true string counterfactuals.<n>We show that the approach produces meaningful counterfactuals while at the same time showing that commonly used intervention techniques have considerable undesired side effects.
  arXiv  Detail & Related papers  (2024-11-11T17:57:30Z)
• On the Proper Treatment of Tokenization in Psycholinguistics [53.960910019072436]
  The paper argues that token-level language models should be marginalized into character-level language models before they are used in psycholinguistic studies.<n>We find various focal areas whose surprisal is a better psychometric predictor than the surprisal of the region of interest itself.
  arXiv  Detail & Related papers  (2024-10-03T17:18:03Z)
• Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive [77.71680953280436]
  The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics. The semantic properties considered are telicity (also combined with definiteness) and agentivity.
  arXiv  Detail & Related papers  (2023-07-06T10:52:22Z)
• Equivariance with Learned Canonicalization Functions [77.32483958400282]
  We show that learning a small neural network to perform canonicalization is better than using predefineds. Our experiments show that learning the canonicalization function is competitive with existing techniques for learning equivariant functions across many tasks.
  arXiv  Detail & Related papers  (2022-11-11T21:58:15Z)
• Interpreting Language Models with Contrastive Explanations [99.7035899290924]
  Language models must consider various features to predict a token, such as its part of speech, number, tense, or semantics. Existing explanation methods conflate evidence for all these features into a single explanation, which is less interpretable for human understanding. We show that contrastive explanations are quantifiably better than non-contrastive explanations in verifying major grammatical phenomena.
  arXiv  Detail & Related papers  (2022-02-21T18:32:24Z)
• Oracle Linguistic Graphs Complement a Pretrained Transformer Language Model: A Cross-formalism Comparison [13.31232311913236]
  We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. We find that, overall, semantic constituency structures are most useful to language modeling performance.
  arXiv  Detail & Related papers  (2021-12-15T04:29:02Z)
• Instance-Based Neural Dependency Parsing [56.63500180843504]
  We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set.
  arXiv  Detail & Related papers  (2021-09-28T05:30:52Z)
• Modeling morphology with Linear Discriminative Learning: considerations and design choices [1.3535770763481905]
  This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning. We illustrate how decisions made about the representation of form and meaning influence model performance. We discuss how the model can be set up to approximate the learning of inflected words in context.
  arXiv  Detail & Related papers  (2021-06-15T07:37:52Z)
• Linguistically inspired morphological inflection with a sequence to sequence model [19.892441884896893]
  Our research question is whether a neural network would be capable of learning inflectional morphemes for inflection production. We are using an inflectional corpus and a single layer seq2seq model to test this hypothesis. Our character-morpheme-based model creates inflection by predicting the stem character-to-character and the inflectional affixes as character blocks.
  arXiv  Detail & Related papers  (2020-09-04T08:58:42Z)
• Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? [41.649440404203595]
  We introduce a method for evaluating whether neural models can learn systematicity of monotonicity inference in natural language. We consider four aspects of monotonicity inferences and test whether the models can systematically interpret lexical and logical phenomena on different training/test splits.
  arXiv  Detail & Related papers  (2020-04-30T14:48:39Z)
• A Simple Joint Model for Improved Contextual Neural Lemmatization [60.802451210656805]
  We present a simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages. Our paper describes the model in addition to training and decoding procedures.
  arXiv  Detail & Related papers  (2019-04-04T02:03:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.