# The SIGMORPHON 2022 Shared Task on Morpheme Segmentation

arXiv: https://arxiv.org/abs/2206.07615 · HTML: https://mlsanity.com/paper/2206.07615/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 82 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://mlsanity.com/paper/1910.13267/) (2019)
- [Charformer: Fast Character Transformers via Gradient-based Subword Tokenization](https://mlsanity.com/paper/2106.12672/) (2021)
- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015)
- [Comparative Study of CNN and RNN for Natural Language Processing](https://mlsanity.com/paper/1702.01923/) (2017)
- [Multi-view Subword Regularization](https://mlsanity.com/paper/2103.08490/) (2021)
- [Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation](https://mlsanity.com/paper/2005.06606/) (2020)
- [KinyaBERT: a Morphology-aware Kinyarwanda Language Model](https://mlsanity.com/paper/2203.08459/) (2022)
- [How Much Does Tokenization Affect Neural Machine Translation?](https://mlsanity.com/paper/1812.08621/) (2018)
- [Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English](https://mlsanity.com/paper/1707.09879/) (2017)
- [Improving Tokenisation by Alternative Treatment of Spaces](https://mlsanity.com/paper/2204.04058/) (2022)
- [Morphological and Language-Agnostic Word Segmentation for NMT](https://mlsanity.com/paper/1806.05482/) (2018)
- [The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2103.11189/) (2021)
- [Joint Optimization of Tokenization and Downstream Model](https://mlsanity.com/paper/2105.12410/) (2021)
- [Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages](https://mlsanity.com/paper/1804.06024/) (2018)
- [Morfessor EM+Prune: Improved Subword Segmentation with Expectation Maximization and Pruning](https://mlsanity.com/paper/2003.03131/) (2020)
- [UniMorph 4.0: Universal Morphology](https://mlsanity.com/paper/2205.03608/) (2022)
- [Imitation Learning for Neural Morphological String Transduction](https://mlsanity.com/paper/1808.10701/) (2018)
- [Neural Polysynthetic Language Modelling](https://mlsanity.com/paper/2005.05477/) (2020)
- [Word Segmentation and Morphological Parsing for Sanskrit](https://mlsanity.com/paper/2201.12833/) (2022)

## Built on by (most cited first, 25 of 25)

- [Analyzing Cognitive Plausibility of Subword Tokenization](https://mlsanity.com/paper/2310.13348/) (2023)
- [MorphBPE: A Morpho-Aware Tokenizer Bridging Linguistic Complexity for Efficient LLM Training Across Morphologies](https://mlsanity.com/paper/2502.00894/) (2025)
- [Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship?](https://mlsanity.com/paper/2310.17774/) (2023)
- [Lexically Grounded Subword Segmentation](https://mlsanity.com/paper/2406.13560/) (2024)
- [Effects of sub-word segmentation on performance of transformer language models](https://mlsanity.com/paper/2305.05480/) (2023)
- [From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes](https://mlsanity.com/paper/2410.22906/) (2024)
- [Biomedical Language Models are Robust to Sub-optimal Tokenization](https://mlsanity.com/paper/2306.17649/) (2023)
- [MorphPiece : A Linguistic Tokenizer for Large Language Models](https://mlsanity.com/paper/2307.07262/) (2023)
- [See the Text: From Tokenization to Visual Reading](https://mlsanity.com/paper/2510.18840/) (2025)
- [Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge](https://mlsanity.com/paper/2404.13292/) (2024)
- [CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models](https://mlsanity.com/paper/2305.14214/) (2023)
- [Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation](https://mlsanity.com/paper/2509.10644/) (2025)
- [Understanding Subword Compositionality of Large Language Models](https://mlsanity.com/paper/2508.17953/) (2025)
- [Revisiting subword tokenization: A case study on affixal negation in large language models](https://mlsanity.com/paper/2404.02421/) (2024)
- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [Incorporating Domain Knowledge into Materials Tokenization](https://mlsanity.com/paper/2506.11115/) (2025)
- [The Learning Dynamics of Subword Segmentation for Morphologically Diverse Languages](https://mlsanity.com/paper/2511.09197/) (2025)
- [Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection](https://mlsanity.com/paper/2409.14628/) (2024)
- [Contextual morphologically-guided tokenization for Latin encoder models](https://mlsanity.com/paper/2511.09709/) (2025)
- [Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features](https://mlsanity.com/paper/2601.18536/) (2026)
- [Team Ryu's Submission to SIGMORPHON 2024 Shared Task on Subword Tokenization](https://mlsanity.com/paper/2410.17094/) (2024)
- [Improving Low-Resource Morphological Inflection via Self-Supervised Objectives](https://mlsanity.com/paper/2506.05227/) (2025)
- [Morpheme Induction for Emergent Language](https://mlsanity.com/paper/2510.03439/) (2025)
- [Comonadic Morphophonology: A Compositional Framework for Context-Dependent Morphological Rules in Finnish](https://mlsanity.com/paper/2605.28484/) (2026)
- [Subword Segmental BabyLMs: Learning to Tokenise for Sample-Efficient Pretraining](https://mlsanity.com/paper/2609.01151/) (2026)

## Abstract (arXiv)

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.