# Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The Case of Fon Language

arXiv: https://arxiv.org/abs/2103.08052 · HTML: https://mlsanity.com/paper/2103.08052/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 14 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Universal Neural Machine Translation for Extremely Low Resource Languages](https://mlsanity.com/paper/1802.05368/) (2018)
- [Revisiting Low-Resource Neural Machine Translation: A Case Study](https://mlsanity.com/paper/1905.11901/) (2019)
- [Masakhane -- Machine Translation For Africa](https://mlsanity.com/paper/2003.11529/) (2020)
- [Using Monolingual Data in Neural Machine Translation: a Systematic Study](https://mlsanity.com/paper/1903.11437/) (2019)
- [The University of Edinburgh's Neural MT Systems for WMT17](https://mlsanity.com/paper/1708.00726/) (2017)
- [Morphological and Language-Agnostic Word Segmentation for NMT](https://mlsanity.com/paper/1806.05482/) (2018)
- [On Optimal Transformer Depth for Low-Resource Language Translation](https://mlsanity.com/paper/2004.04418/) (2020)
- [Towards Neural Machine Translation for African Languages](https://mlsanity.com/paper/1811.05467/) (2018)
- [Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation](https://mlsanity.com/paper/1910.03467/) (2019)
- [FFR V1.0: Fon-French Neural Machine Translation](https://mlsanity.com/paper/2003.12111/) (2020)

## Built on by (most cited first, 6 of 6)

- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [Impact of Tokenization on Language Models: An Analysis for Turkish](https://mlsanity.com/paper/2204.08832/) (2022)
- [MMTAfrica: Multilingual Machine Translation for African Languages](https://mlsanity.com/paper/2204.04306/) (2022)
- [Towards Open-Ended Discovery for Low-Resource NLP](https://mlsanity.com/paper/2510.01220/) (2025)
- [A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development](https://mlsanity.com/paper/2605.22828/) (2026)

## Abstract (arXiv)

Building effective neural machine translation (NMT) models for very low-resourced and morphologically rich African indigenous languages is an open challenge. Besides the issue of finding available resources for them, a lot of work is put into preprocessing and tokenization. Recent studies have shown that standard tokenization methods do not always adequately deal with the grammatical, diacritical, and tonal properties of some African languages. That, coupled with the extremely low availability of training samples, hinders the production of reliable NMT models. In this paper, using Fon language as a case study, we revisit standard tokenization methods and introduce Word-Expressions-Based (WEB) tokenization, a human-involved super-words tokenization strategy to create a better representative vocabulary for training. Furthermore, we compare our tokenization strategy to others on the Fon-French and French-Fon translation tasks.