# The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection

arXiv: https://arxiv.org/abs/1910.11493 · HTML: https://mlsanity.com/paper/1910.11493/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 30 of 43)

- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance](https://mlsanity.com/paper/2108.05682/) (2021)
- [Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects](https://mlsanity.com/paper/2110.06852/) (2021)
- [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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- [UniMorph 4.0: Universal Morphology](https://mlsanity.com/paper/2205.03608/) (2022)
- [A multilabel approach to morphosyntactic probing](https://mlsanity.com/paper/2104.08464/) (2021)
- [What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity](https://mlsanity.com/paper/2204.05056/) (2022)
- [CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models](https://mlsanity.com/paper/2305.14214/) (2023)
- [Eeny, meeny, miny, moe. How to choose data for morphological inflection](https://mlsanity.com/paper/2210.14465/) (2022)
- [On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions](https://mlsanity.com/paper/2103.01273/) (2021)
- [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)
- [On the Role of Morphological Information for Contextual Lemmatization](https://mlsanity.com/paper/2302.00407/) (2023)
- [Solving morphological analogies: from retrieval to generation](https://mlsanity.com/paper/2303.18062/) (2023)
- [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)
- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [Minimal Supervision for Morphological Inflection](https://mlsanity.com/paper/2104.08512/) (2021)
- [Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation](https://mlsanity.com/paper/2105.03432/) (2021)
- [A Three Step Training Approach with Data Augmentation for Morphological Inflection](https://mlsanity.com/paper/2109.07006/) (2021)
- [The Construction and Evaluation of the LEAFTOP Dataset of Automatically Extracted Nouns in 1480 Languages](https://mlsanity.com/paper/2206.05034/) (2022)
- [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)
- [Evaluating Shortest Edit Script Methods for Contextual Lemmatization](https://mlsanity.com/paper/2403.16968/) (2024)
- [Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection](https://mlsanity.com/paper/2409.14628/) (2024)
- [RUMLEM: A Dictionary-Based Lemmatizer for Romansh](https://mlsanity.com/paper/2604.11233/) (2026)
- [Measuring Form and Function in Language Models](https://mlsanity.com/paper/2605.28616/) (2026)

## Abstract (arXiv)

The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years' inflection tasks by examining transfer of morphological inflection knowledge from a high-resource language to a low-resource language. This year also presents a new second challenge on lemmatization and morphological feature analysis in context. All submissions featured a neural component and built on either this year's strong baselines or highly ranked systems from previous years' shared tasks. Every participating team improved in accuracy over the baselines for the inflection task (though not Levenshtein distance), and every team in the contextual analysis task improved on both state-of-the-art neural and non-neural baselines.