# The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation

arXiv: https://arxiv.org/abs/2103.11189 · HTML: https://mlsanity.com/paper/2103.11189/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 20 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Revisiting Low-Resource Neural Machine Translation: A Case Study](https://mlsanity.com/paper/1905.11901/) (2019)
- [Character-based Neural Machine Translation](https://mlsanity.com/paper/1603.00810/) (2016)
- [Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English](https://mlsanity.com/paper/1707.09879/) (2017)
- [Compositional Representation of Morphologically-Rich Input for Neural Machine Translation](https://mlsanity.com/paper/1805.02036/) (2018)
- [Cognate-aware morphological segmentation for multilingual neural translation](https://mlsanity.com/paper/1808.10791/) (2018)

## Built on by (most cited first, 16 of 16)

- [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)
- [Why do language models perform worse for morphologically complex languages?](https://mlsanity.com/paper/2411.14198/) (2024)
- [The SIGMORPHON 2022 Shared Task on Morpheme Segmentation](https://mlsanity.com/paper/2206.07615/) (2022)
- [Evaluating Morphological Alignment of Tokenizers in 70 Languages](https://mlsanity.com/paper/2507.06378/) (2025)
- [BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages](https://mlsanity.com/paper/2203.08954/) (2022)
- [The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?](https://mlsanity.com/paper/2601.07220/) (2026)
- [MorphPiece : A Linguistic Tokenizer for Large Language Models](https://mlsanity.com/paper/2307.07262/) (2023)
- [Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text](https://mlsanity.com/paper/2210.06990/) (2022)
- [CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models](https://mlsanity.com/paper/2305.14214/) (2023)
- [Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://mlsanity.com/paper/2306.06804/) (2023)
- [Revisiting subword tokenization: A case study on affixal negation in large language models](https://mlsanity.com/paper/2404.02421/) (2024)
- [Morphological Typology in BPE Subword Productivity and Language Modeling](https://mlsanity.com/paper/2410.23656/) (2024)
- [Benchmarking Azerbaijani Neural Machine Translation](https://mlsanity.com/paper/2207.14473/) (2022)
- [Unsupervised Morphological Tree Tokenizer](https://mlsanity.com/paper/2406.15245/) (2024)
- [MoVoC: Morphology-Aware Subword Construction for Geez Script Languages](https://mlsanity.com/paper/2509.08812/) (2025)

## Abstract (arXiv)

This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting. We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL. We evaluate translation tasks between English and each of Nepali, Sinhala, and Kazakh, and predict that using morphologically-based segmentation methods would lead to better performance in this setting. However, comparing to BPE, we find that no consistent and reliable differences emerge between the segmentation methods. While morphologically-based methods outperform BPE in a few cases, what performs best tends to vary across tasks, and the performance of segmentation methods is often statistically indistinguishable.