# Morphological Inflection Generation Using Character Sequence to Sequence Learning

arXiv: https://arxiv.org/abs/1512.06110 · HTML: https://mlsanity.com/paper/1512.06110/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2015.

## Built on by (most cited first, 14 of 14)

- [Smoothing and Shrinking the Sparse Seq2Seq Search Space](https://mlsanity.com/paper/2103.10291/) (2021)
- [CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models](https://mlsanity.com/paper/2305.14214/) (2023)
- [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)
- [Rule-based Morphological Inflection Improves Neural Terminology Translation](https://mlsanity.com/paper/2109.04620/) (2021)
- [An Investigation of Noise in Morphological Inflection](https://mlsanity.com/paper/2305.16581/) (2023)
- [Searching for Search Errors in Neural Morphological Inflection](https://mlsanity.com/paper/2102.08424/) (2021)
- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [Not quite there yet: Combining analogical patterns and encoder-decoder networks for cognitively plausible inflection](https://mlsanity.com/paper/2108.03968/) (2021)
- [Minimal Supervision for Morphological Inflection](https://mlsanity.com/paper/2104.08512/) (2021)
- [A Three Step Training Approach with Data Augmentation for Morphological Inflection](https://mlsanity.com/paper/2109.07006/) (2021)
- [When Irregularity Helps: A Subclass Analysis of Inductive Bias in Neural Morphology](https://mlsanity.com/paper/2605.20558/) (2026)
- [A big data approach towards sarcasm detection in Russian](https://mlsanity.com/paper/2306.00445/) (2023)
- [OOVs in the Spotlight: How to Inflect them?](https://mlsanity.com/paper/2404.08974/) (2024)

## Abstract (arXiv)

Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.