# Unlabeled Data for Morphological Generation With Character-Based Sequence-to-Sequence Models

arXiv: https://arxiv.org/abs/1705.06106 · HTML: https://mlsanity.com/paper/1705.06106/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

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

- [Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models](https://mlsanity.com/paper/2104.06483/) (2021)
- [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)
- [Improving Low-Resource Morphological Inflection via Self-Supervised Objectives](https://mlsanity.com/paper/2506.05227/) (2025)

## Abstract (arXiv)

We present a semi-supervised way of training a character-based encoder-decoder recurrent neural network for morphological reinflection, the task of generating one inflected word form from another. This is achieved by using unlabeled tokens or random strings as training data for an autoencoding task, adapting a network for morphological reinflection, and performing multi-task training. We thus use limited labeled data more effectively, obtaining up to 9.9% improvement over state-of-the-art baselines for 8 different languages.