# The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

arXiv: https://arxiv.org/abs/1810.07125 · HTML: https://mlsanity.com/paper/1810.07125/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 30 of 41)

- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance](https://mlsanity.com/paper/2108.05682/) (2021)
- [Evaluating Morphological Compositional Generalization in Large Language Models](https://mlsanity.com/paper/2410.12656/) (2024)
- [Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem](https://mlsanity.com/paper/2406.15625/) (2024)
- [Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models](https://mlsanity.com/paper/2104.06483/) (2021)
- [UniMorph 4.0: Universal Morphology](https://mlsanity.com/paper/2205.03608/) (2022)
- [Learning Symbolic Rules for Reasoning in Quasi-Natural Language](https://mlsanity.com/paper/2111.12038/) (2021)
- [Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model](https://mlsanity.com/paper/2310.15113/) (2023)
- [What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity](https://mlsanity.com/paper/2204.05056/) (2022)
- [User-Centric Gender Rewriting](https://mlsanity.com/paper/2205.02211/) (2022)
- [CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models](https://mlsanity.com/paper/2305.14214/) (2023)
- [Frequency matters: Modeling irregular morphological patterns in Spanish with Transformers](https://mlsanity.com/paper/2410.21013/) (2024)
- [Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study](https://mlsanity.com/paper/2203.08527/) (2022)
- [Morphological Processing of Low-Resource Languages: Where We Are and What's Next](https://mlsanity.com/paper/2203.08909/) (2022)
- [K-UniMorph: Korean Universal Morphology and its Feature Schema](https://mlsanity.com/paper/2305.06335/) (2023)
- [Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation](https://mlsanity.com/paper/2509.10644/) (2025)
- [Rule-based Morphological Inflection Improves Neural Terminology Translation](https://mlsanity.com/paper/2109.04620/) (2021)
- [Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflection](https://mlsanity.com/paper/2305.13658/) (2023)
- [Cross-Linguistic Transfer in Multilingual NLP: The Role of Language Families and Morphology](https://mlsanity.com/paper/2505.13908/) (2025)
- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [Morphological Inflection: A Reality Check](https://mlsanity.com/paper/2305.15637/) (2023)
- [Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans](https://mlsanity.com/paper/2602.14100/) (2026)
- [Minimal Supervision for Morphological Inflection](https://mlsanity.com/paper/2104.08512/) (2021)
- [A Three Step Training Approach with Data Augmentation for Morphological Inflection](https://mlsanity.com/paper/2109.07006/) (2021)
- [Language Varieties of Italy: Technology Challenges and Opportunities](https://mlsanity.com/paper/2209.09757/) (2022)
- [Exploring Linguistic Probes for Morphological Generalization](https://mlsanity.com/paper/2310.13686/) (2023)
- [J-UniMorph: Japanese Morphological Annotation through the Universal Feature Schema](https://mlsanity.com/paper/2402.14411/) (2024)
- [Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection](https://mlsanity.com/paper/2409.14628/) (2024)
- [When Irregularity Helps: A Subclass Analysis of Inductive Bias in Neural Morphology](https://mlsanity.com/paper/2605.20558/) (2026)
- [Measuring Form and Function in Language Models](https://mlsanity.com/paper/2605.28616/) (2026)

## Abstract (arXiv)

The CoNLL--SIGMORPHON 2018 shared task on supervised learning of morphological generation featured data sets from 103 typologically diverse languages. Apart from extending the number of languages involved in earlier supervised tasks of generating inflected forms, this year the shared task also featured a new second task which asked participants to inflect words in sentential context, similar to a cloze task. This second task featured seven languages. Task 1 received 27 submissions and task 2 received 6 submissions. Both tasks featured a low, medium, and high data condition. Nearly all submissions featured a neural component and built on highly-ranked systems from the earlier 2017 shared task. In the inflection task (task 1), 41 of the 52 languages present in last year's inflection task showed improvement by the best systems in the low-resource setting. The cloze task (task 2) proved to be difficult, and few submissions managed to consistently improve upon both a simple neural baseline system and a lemma-repeating baseline.