# A Latent Morphology Model for Open-Vocabulary Neural Machine Translation

arXiv: https://arxiv.org/abs/1910.13890 · HTML: https://mlsanity.com/paper/1910.13890/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Uncertainty in Natural Language Generation: From Theory to Applications](https://mlsanity.com/paper/2307.15703/) (2023)
- [Evaluating the Morphosyntactic Well-formedness of Generated Texts](https://mlsanity.com/paper/2103.16590/) (2021)
- [Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages](https://mlsanity.com/paper/2208.06061/) (2022)
- [Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention](https://mlsanity.com/paper/2102.01223/) (2021)
- [Learning to Abstract with Nonparametric Variational Information Bottleneck](https://mlsanity.com/paper/2310.17284/) (2023)
- [Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts](https://mlsanity.com/paper/2309.12863/) (2023)

## Abstract (arXiv)

Translation into morphologically-rich languages challenges neural machine translation (NMT) models with extremely sparse vocabularies where atomic treatment of surface forms is unrealistic. This problem is typically addressed by either pre-processing words into subword units or performing translation directly at the level of characters. The former is based on word segmentation algorithms optimized using corpus-level statistics with no regard to the translation task. The latter learns directly from translation data but requires rather deep architectures. In this paper, we propose to translate words by modeling word formation through a hierarchical latent variable model which mimics the process of morphological inflection. Our model generates words one character at a time by composing two latent representations: a continuous one, aimed at capturing the lexical semantics, and a set of (approximately) discrete features, aimed at capturing the morphosyntactic function, which are shared among different surface forms. Our model achieves better accuracy in translation into three morphologically-rich languages than conventional open-vocabulary NMT methods, while also demonstrating a better generalization capacity under low to mid-resource settings.