# Morphological Inflection: A Reality Check

arXiv: https://arxiv.org/abs/2305.15637 · HTML: https://mlsanity.com/paper/2305.15637/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 90 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 13 of 13)

- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection](https://mlsanity.com/paper/1910.11493/) (2019)
- [Applying the Transformer to Character-level Transduction](https://mlsanity.com/paper/2005.10213/) (2020)
- [UniMorph 2.0: Universal Morphology](https://mlsanity.com/paper/1810.11101/) (2018)
- [The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection](https://mlsanity.com/paper/1810.07125/) (2018)
- [CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages](https://mlsanity.com/paper/1706.09031/) (2017)
- [SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection](https://mlsanity.com/paper/2006.11572/) (2020)
- [Not always about you: Prioritizing community needs when developing endangered language technology](https://mlsanity.com/paper/2204.05541/) (2022)
- [(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance](https://mlsanity.com/paper/2108.05682/) (2021)
- [From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings](https://mlsanity.com/paper/1802.09375/) (2018)
- [Morphological Irregularity Correlates with Frequency](https://mlsanity.com/paper/1906.11483/) (2019)
- [UniMorph 4.0: Universal Morphology](https://mlsanity.com/paper/2205.03608/) (2022)
- [Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation](https://mlsanity.com/paper/2201.01845/) (2022)

## Built on by (most cited first, 2 of 2)

- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [Flexing in 73 Languages: A Single Small Model for Multilingual Inflection](https://mlsanity.com/paper/2510.23114/) (2025)

## Abstract (arXiv)

Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications. For years now, state-of-the-art systems have reported high, but also highly variable, performance across data sets and languages. We investigate the causes of this high performance and high variability; we find several aspects of data set creation and evaluation which systematically inflate performance and obfuscate differences between languages. To improve generalizability and reliability of results, we propose new data sampling and evaluation strategies that better reflect likely use-cases. Using these new strategies, we make new observations on the generalization abilities of current inflection systems.