# SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection

arXiv: https://arxiv.org/abs/2006.11572 · HTML: https://mlsanity.com/paper/2006.11572/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

## Built on by (most cited first, 30 of 39)

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- [(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)
- [Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models](https://mlsanity.com/paper/2104.06483/) (2021)
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- [How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?](https://mlsanity.com/paper/2109.01100/) (2021)
- [Eeny, meeny, miny, moe. How to choose data for morphological inflection](https://mlsanity.com/paper/2210.14465/) (2022)
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- [Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation](https://mlsanity.com/paper/2509.10644/) (2025)
- [An Investigation of Noise in Morphological Inflection](https://mlsanity.com/paper/2305.16581/) (2023)
- [What is "Typological Diversity" in NLP?](https://mlsanity.com/paper/2402.04222/) (2024)
- [Searching for Search Errors in Neural Morphological Inflection](https://mlsanity.com/paper/2102.08424/) (2021)
- [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)
- [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)
- [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)
- [OOVs in the Spotlight: How to Inflect them?](https://mlsanity.com/paper/2404.08974/) (2024)

## Abstract (arXiv)

A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems' ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90% mean accuracy while others were more challenging.