# CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages

arXiv: https://arxiv.org/abs/1706.09031 · HTML: https://mlsanity.com/paper/1706.09031/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2017.

## Built on by (most cited first, 30 of 40)

- [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)
- [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)
- [The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?](https://mlsanity.com/paper/2601.07220/) (2026)
- [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)
- [Morphosyntactic probing of multilingual BERT models](https://mlsanity.com/paper/2306.06205/) (2023)
- [Autoregressive Modeling with Lookahead Attention](https://mlsanity.com/paper/2305.12272/) (2023)
- [Frequency matters: Modeling irregular morphological patterns in Spanish with Transformers](https://mlsanity.com/paper/2410.21013/) (2024)
- [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)
- [Rule-based Morphological Inflection Improves Neural Terminology Translation](https://mlsanity.com/paper/2109.04620/) (2021)
- [An Investigation of Noise in Morphological Inflection](https://mlsanity.com/paper/2305.16581/) (2023)
- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [Morphology Without Borders: Clause-Level Morphology](https://mlsanity.com/paper/2202.12832/) (2022)
- [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)
- [Coloring the Black Box: What Synesthesia Tells Us about Character Embeddings](https://mlsanity.com/paper/2101.10565/) (2021)
- [Do RNN States Encode Abstract Phonological Processes?](https://mlsanity.com/paper/2104.00789/) (2021)
- [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)
- [Context based lemmatizer for Polish language](https://mlsanity.com/paper/2207.11565/) (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)
- [Contextual morphologically-guided tokenization for Latin encoder models](https://mlsanity.com/paper/2511.09709/) (2025)
- [When Irregularity Helps: A Subclass Analysis of Inductive Bias in Neural Morphology](https://mlsanity.com/paper/2605.20558/) (2026)
- [Differentiable Generative Phonology](https://mlsanity.com/paper/2102.05717/) (2021)
- [A Framework for Bidirectional Decoding: Case Study in Morphological Inflection](https://mlsanity.com/paper/2305.12580/) (2023)
- [Morphological Inflection with Phonological Features](https://mlsanity.com/paper/2306.12581/) (2023)

## Abstract (arXiv)

The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages. In sub-task 1, submitted systems were asked to predict a specific inflected form of a given lemma. In sub-task 2, systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms. Both sub-tasks included high, medium, and low-resource conditions. Sub-task 1 received 24 system submissions, while sub-task 2 received 3 system submissions. Following the success of neural sequence-to-sequence models in the SIGMORPHON 2016 shared task, all but one of the submissions included a neural component. The results show that high performance can be achieved with small training datasets, so long as models have appropriate inductive bias or make use of additional unlabeled data or synthetic data. However, different biasing and data augmentation resulted in disjoint sets of inflected forms being predicted correctly, suggesting that there is room for future improvement.