| Challenge: | a new measure of irregularity is proposed for inflectional morphology of languages . the measure is based on the predictability of forms in a language . |
| Approach: | They propose a measure of irregularity based on the predictability of forms in a language. |
| Outcome: | The proposed measure is the first of its breadth and confirms longstanding proposals from the linguistics literature. |
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Error Analysis and the Role of Morphology (2021.eacl-main)
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| Challenge: | Using morphological features does improve error prediction across tasks, but is less pronounced in morphology-complex languages. |
| Approach: | They propose to use morphological features to improve error prediction across four different tasks and up to 57 languages to test their hypothesis. |
| Outcome: | The proposed model is more discriminative in morphologically simple languages than in simple ones. |
Morphological Inflection: A Reality Check (2023.acl-long)
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| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
| Approach: | They propose new methods to analyze data sets and evaluate their generalization abilities to better reflect likely use-cases. |
| Outcome: | The proposed methods improve generalizability and reliability of results and improve generalization abilities. |
Frequency matters: Modeling irregular morphological patterns in Spanish with Transformers (2025.findings-acl)
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| Challenge: | A common generation task in morphology is morphological inflection, where a target form has to be generated from its corresponding lemma and feature tag. |
| Approach: | They propose to solve the Paradigm Cell Filling Problem (PCFP) by using encoder-decoder transformers to generate inflected verbs in Spanish. |
| Outcome: | The proposed model performs better on L-shaped verbs than regular verbs, but no consistent recency effects are observed. |
Morphology Matters: A Multilingual Language Modeling Analysis (2021.tacl-1)
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| Challenge: | Existing studies on inflectional morphology disagree on whether or not it makes languages harder to model. |
| Approach: | They propose to use a corpus of 145 Bible translations in 92 languages to investigate whether inflectional morphology makes languages harder to model. |
| Outcome: | The proposed model trains with linguistically motivated subword segmentation strategies and reduces the impact of morphology on language modeling. |
Exploring Linguistic Probes for Morphological Inflection (2023.emnlp-main)
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| Challenge: | morphological inflection models typically employ language-independent data splitting algorithms. |
| Approach: | They propose language-specific probes to test aspects of morphological generalization . they use three morphology-distinct languages to test their generalization abilities . |
| Outcome: | The proposed language-specific probes are used to test morphological generalization abilities on three distinct languages. |
A Morphology-Based Investigation of Positional Encodings (2024.emnlp-main)
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| Challenge: | Contemporary deep learning models handle languages with diverse morphology . morphological complexity of languages is closely linked with positional encodings . |
| Approach: | They propose to use positional encodings to integrate morphological complexity into deep learning models. |
| Outcome: | The proposed model improves on 22 languages and 5 downstream tasks. |
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection (2022.emnlp-main)
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| Challenge: | Neural networks are at the center of a debate about human behavior in inflectional morphology. |
| Approach: | They measure correlation between human judgments and neural network probabilities for unknown word inflections. |
| Outcome: | The proposed model for morphological inflections correlates best with human wug ratings, but not with humans. |
Meaning to Form: Measuring Systematicity as Information (P19-1)
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| Challenge: | A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between word forms and their meaning, or does some systematic phenomenon pervade? |
| Approach: | They propose to quantify the systematicity of the sign using mutual information and recurrent neural networks to examine 106 languages. |
| Outcome: | The proposed model reduces entropy in a word form conditioned on its semantic representation and recovers English examples of systematic affixes. |
Word Frequency Does Not Predict Grammatical Knowledge in Language Models (2020.emnlp-main)
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| Challenge: | Neural language models learn the grammatical properties of natural languages to varying degrees of accuracy. |
| Approach: | They focus on subject-verb agreement and reflexive anaphora to investigate whether there are systematic sources of variation in the language models’ accuracy. |
| Outcome: | The proposed model can learn grammatical properties from training data. |
Confounding Factors in Relating Model Performance to Morphology (2025.emnlp-main)
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| Challenge: | morphological differences between languages are unclear, but are often considered unimportant . confounding factors make it hard to compare results and draw conclusions, authors argue . |
| Approach: | They propose to use token bigram metrics to predict difficulty of causal language modeling . they argue that confounding factors are contributing to the conflicting evidence . |
| Outcome: | The proposed metrics better capture the relation between morphology and tokenization compared to word-based models. |