# Paradigm Completion for Derivational Morphology

arXiv: https://arxiv.org/abs/1708.09151 · HTML: https://mlsanity.com/paper/1708.09151/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 5 of 5)

- [Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words](https://mlsanity.com/paper/2101.00403/) (2021)
- [Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model](https://mlsanity.com/paper/2310.15113/) (2023)
- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [A Derivational ChainBank for Modern Standard Arabic](https://mlsanity.com/paper/2410.20463/) (2024)

## Abstract (arXiv)

The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models, adapted from the inflection task, are able to learn a range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.