# BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages

arXiv: https://arxiv.org/abs/2203.08954 · HTML: https://mlsanity.com/paper/2203.08954/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 60 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [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)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Challenges of language technologies for the indigenous languages of the Americas](https://mlsanity.com/paper/1806.04291/) (2018)
- [Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English](https://mlsanity.com/paper/1707.09879/) (2017)
- [The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2103.11189/) (2021)
- [From Characters to Words to in Between: Do We Capture Morphology?](https://mlsanity.com/paper/1704.08352/) (2017)
- [Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages](https://mlsanity.com/paper/1804.06024/) (2018)
- [Tackling the Low-resource Challenge for Canonical Segmentation](https://mlsanity.com/paper/2010.02804/) (2020)
- [An Unsupervised Method for Uncovering Morphological Chains](https://mlsanity.com/paper/1503.02335/) (2015)
- [Neural Polysynthetic Language Modelling](https://mlsanity.com/paper/2005.05477/) (2020)

## Built on by (most cited first, 14 of 14)

- [Greed is All You Need: An Evaluation of Tokenizer Inference Methods](https://mlsanity.com/paper/2403.01289/) (2024)
- [Analyzing Cognitive Plausibility of Subword Tokenization](https://mlsanity.com/paper/2310.13348/) (2023)
- [The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?](https://mlsanity.com/paper/2601.07220/) (2026)
- [Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text](https://mlsanity.com/paper/2210.06990/) (2022)
- [Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://mlsanity.com/paper/2306.06804/) (2023)
- [The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations](https://mlsanity.com/paper/2402.14616/) (2024)
- [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://mlsanity.com/paper/2311.00658/) (2023)
- [NLP Progress in Indigenous Latin American Languages](https://mlsanity.com/paper/2404.05365/) (2024)
- [AdaptBPE: From General Purpose to Specialized Tokenizers](https://mlsanity.com/paper/2601.21665/) (2026)
- [The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation](https://mlsanity.com/paper/2404.09371/) (2024)
- [Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic](https://mlsanity.com/paper/2510.17001/) (2025)
- [Benchmarking Azerbaijani Neural Machine Translation](https://mlsanity.com/paper/2207.14473/) (2022)
- [KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis](https://mlsanity.com/paper/2304.12569/) (2023)
- [Team Ryu's Submission to SIGMORPHON 2024 Shared Task on Subword Tokenization](https://mlsanity.com/paper/2410.17094/) (2024)

## Abstract (arXiv)

Morphologically-rich polysynthetic languages present a challenge for NLP systems due to data sparsity, and a common strategy to handle this issue is to apply subword segmentation. We investigate a wide variety of supervised and unsupervised morphological segmentation methods for four polysynthetic languages: Nahuatl, Raramuri, Shipibo-Konibo, and Wixarika. Then, we compare the morphologically inspired segmentation methods against Byte-Pair Encodings (BPEs) as inputs for machine translation (MT) when translating to and from Spanish. We show that for all language pairs except for Nahuatl, an unsupervised morphological segmentation algorithm outperforms BPEs consistently and that, although supervised methods achieve better segmentation scores, they under-perform in MT challenges. Finally, we contribute two new morphological segmentation datasets for Raramuri and Shipibo-Konibo, and a parallel corpus for Raramuri--Spanish.