# Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection

arXiv: https://arxiv.org/abs/1606.00589 · HTML: https://mlsanity.com/paper/1606.00589/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2016.

## Built on by (most cited first, 12 of 12)

- [User-Centric Gender Rewriting](https://mlsanity.com/paper/2205.02211/) (2022)
- [Frequency matters: Modeling irregular morphological patterns in Spanish with Transformers](https://mlsanity.com/paper/2410.21013/) (2024)
- [Morphological Processing of Low-Resource Languages: Where We Are and What's Next](https://mlsanity.com/paper/2203.08909/) (2022)
- [An Investigation of Noise in Morphological Inflection](https://mlsanity.com/paper/2305.16581/) (2023)
- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection](https://mlsanity.com/paper/2210.12321/) (2022)
- [Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans](https://mlsanity.com/paper/2602.14100/) (2026)
- [Coloring the Black Box: What Synesthesia Tells Us about Character Embeddings](https://mlsanity.com/paper/2101.10565/) (2021)
- [When Irregularity Helps: A Subclass Analysis of Inductive Bias in Neural Morphology](https://mlsanity.com/paper/2605.20558/) (2026)
- [OOVs in the Spotlight: How to Inflect them?](https://mlsanity.com/paper/2404.08974/) (2024)
- [GLeMM: A large-scale multilingual dataset for morphological research](https://mlsanity.com/paper/2604.12442/) (2026)
- [Not All Irregularity Is Equal: Causally Isolating a Rare Failure Mode in Japanese Morphological Inflection](https://mlsanity.com/paper/2609.21179/) (2026)

## Abstract (arXiv)

Morphological reinflection is the task of generating a target form given a source form, a source tag and a target tag. We propose a new way of modeling this task with neural encoder-decoder models. Our approach reduces the amount of required training data for this architecture and achieves state-of-the-art results, making encoder-decoder models applicable to morphological reinflection even for low-resource languages. We further present a new automatic correction method for the outputs based on edit trees.