# Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation

arXiv: https://arxiv.org/abs/2001.01589 · HTML: https://mlsanity.com/paper/2001.01589/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [MorphPiece : A Linguistic Tokenizer for Large Language Models](https://mlsanity.com/paper/2307.07262/) (2023)
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- [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)
- [Tokens with Meaning: A Hybrid Tokenization Approach for Turkish](https://mlsanity.com/paper/2508.14292/) (2025)
- [How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?](https://mlsanity.com/paper/2208.05225/) (2022)
- [ImmunoLingo: Linguistics-based formalization of the antibody language](https://mlsanity.com/paper/2209.12635/) (2022)
- [Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!](https://mlsanity.com/paper/2403.10963/) (2024)
- [How Important Is Tokenization in French Medical Masked Language Models?](https://mlsanity.com/paper/2402.15010/) (2024)
- [From Smor-re-brod to Subwords: Training LLMs on Danish, One Morpheme at a Time](https://mlsanity.com/paper/2504.01540/) (2025)

## Abstract (arXiv)

Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years. However, in consideration of efficiency, a limited-size vocabulary that only contains the top-N highest frequency words are employed for model training, which leads to many rare and unknown words. It is rather difficult when translating from the low-resource and morphologically-rich agglutinative languages, which have complex morphology and large vocabulary. In this paper, we propose a morphological word segmentation method on the source-side for NMT that incorporates morphology knowledge to preserve the linguistic and semantic information in the word structure while reducing the vocabulary size at training time. It can be utilized as a preprocessing tool to segment the words in agglutinative languages for other natural language processing (NLP) tasks. Experimental results show that our morphologically motivated word segmentation method is better suitable for the NMT model, which achieves significant improvements on Turkish-English and Uyghur-Chinese machine translation tasks on account of reducing data sparseness and language complexity.