Marrying Universal Dependencies and Universal Morphology
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Abstract (arXiv)
The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages - UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project's annotations could be used to validate the other's. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13% recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Oct 2018.
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Citing papers per year: 2021: 3; 2022: 5; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
11 papers.
- 2021On the Pitfalls of Analyzing Individual Neurons in Language Modelscited by 61
- 2024Probing the Emergence of Cross-lingual Alignment during LLM Trainingcited by 38
- 2022Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Modelscited by 14
- 2021Probing as Quantifying Inductive Biascited by 11
- 2022UniMorph 4.0: Universal Morphologycited by 11
- 2021Evaluating the Morphosyntactic Well-formedness of Generated Textscited by 8
- 2022A Latent-Variable Model for Intrinsic Probingcited by 5
- 2023A Joint Matrix Factorization Analysis of Multilingual Representationscited by 5
- 2022Morphological Processing of Low-Resource Languages: Where We Are and What's Nextcited by 4
- 2022Cross-lingual Inflection as a Data Augmentation Method for Parsingcited by 0
- 2026Zero-Shot Morphological Discovery in Low-Resource Bantu Languages via Cross-Lingual Transfer and Unsupervised Clusteringcited by 0
- 2026Zero-Shot Morphological Discovery in Low-Resource Bantu Languages via Cross-Lingual Transfer and Unsupervised Clustering24 Apr 2026
- 2024Probing the Emergence of Cross-lingual Alignment during LLM Training19 Jun 2024
- 2023A Joint Matrix Factorization Analysis of Multilingual Representations24 Oct 2023
- 2022Cross-lingual Inflection as a Data Augmentation Method for Parsing19 May 2022
- 2022UniMorph 4.0: Universal Morphology7 May 2022
- 2022Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models4 May 2022
- 2022Morphological Processing of Low-Resource Languages: Where We Are and What's Next16 Mar 2022
- 2022A Latent-Variable Model for Intrinsic Probing20 Jan 2022
- 2021Probing as Quantifying Inductive Bias15 Oct 2021
- 2021On the Pitfalls of Analyzing Individual Neurons in Language Models14 Oct 2021
- 2021Evaluating the Morphosyntactic Well-formedness of Generated Texts30 Mar 2021