mlsanityData to 14 Oct 2026

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

arXiv 1810.07125 ↗First posted 16 Oct 2018cs.CLcited by 41

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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.

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

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Citing papers per year: 2021: 8; 2022: 6; 2023: 8; 2024: 6; 2025: 5; 2026: 8.

41 papers.

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  1. 2021Systematic Inequalities in Language Technology Performance across the World's Languagescited by 120
  2. 2021(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performancecited by 20
  3. 2024Evaluating Morphological Compositional Generalization in Large Language Modelscited by 19
  4. 2024Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problemcited by 17
  5. 2021Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Modelscited by 13
  6. 2022UniMorph 4.0: Universal Morphologycited by 11
  7. 2021Learning Symbolic Rules for Reasoning in Quasi-Natural Languagecited by 9
  8. 2023Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Modelcited by 9
  9. 2022What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexitycited by 8
  10. 2022User-Centric Gender Rewritingcited by 7
  11. 2023CompoundPiece: Evaluating and Improving Decompounding Performance of Language Modelscited by 7
  12. 2024Frequency matters: Modeling irregular morphological patterns in Spanish with Transformerscited by 5
29 more
  1. 2022Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Studycited by 4
  2. 2022Morphological Processing of Low-Resource Languages: Where We Are and What's Nextcited by 4
  3. 2023K-UniMorph: Korean Universal Morphology and its Feature Schemacited by 4
  4. 2025Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentationcited by 4
  5. 2021Rule-based Morphological Inflection Improves Neural Terminology Translationcited by 3
  6. 2023Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflectioncited by 3
  7. 2025Cross-Linguistic Transfer in Multilingual NLP: The Role of Language Families and Morphologycited by 3
  8. 2021Computational Morphology with Neural Network Approachescited by 2
  9. 2023Morphological Inflection: A Reality Checkcited by 2
  10. 2026Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humanscited by 2
  11. 2021Minimal Supervision for Morphological Inflectioncited by 1
  12. 2021A Three Step Training Approach with Data Augmentation for Morphological Inflectioncited by 1
  13. 2022Language Varieties of Italy: Technology Challenges and Opportunitiescited by 1
  14. 2023Exploring Linguistic Probes for Morphological Generalizationcited by 1
  15. 2024J-UniMorph: Japanese Morphological Annotation through the Universal Feature Schemacited by 1
  16. 2024Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collectioncited by 1
  17. 2026When Irregularity Helps: A Subclass Analysis of Inductive Bias in Neural Morphologycited by 1
  18. 2026Measuring Form and Function in Language Modelscited by 1
  19. 2026YOMI-Bench: A Benchmark for Evaluating Kanji Reading and Phonological Understanding of LLMs for Japanesecited by 1
  20. 2023A Framework for Bidirectional Decoding: Case Study in Morphological Inflectioncited by 0
  21. 2023Morphological Inflection with Phonological Featurescited by 0
  22. 2024OOVs in the Spotlight: How to Inflect them?cited by 0
  23. 2025Transformers over-extend what humans underlearn: the case of Spanish L-shaped morphomecited by 0
  24. 2025Corpus Frequencies in Morphological Inflection: Do They Matter?cited by 0
  25. 2025Learning inflection classes using Adaptive Resonance Theorycited by 0
  26. 2026Zero-Shot Morphological Discovery in Low-Resource Bantu Languages via Cross-Lingual Transfer and Unsupervised Clusteringcited by 0
  27. 2026Probing Character-level Transformers for the Spanish L-shaped Morphomecited by 0
  28. 2026YallaMorph: A Benchmark for Evaluating Arabic Morphological Generation in Large Language Modelscited by 0
  29. 2026Not All Irregularity Is Equal: Causally Isolating a Rare Failure Mode in Japanese Morphological Inflectioncited by 0