# Construction and educational application of a linguistically grounded dependency treebank for Uyghur

arXiv: https://arxiv.org/abs/2507.21536 · HTML: https://mlsanity.com/paper/2507.21536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 75 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 16 of 16)

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- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Is GPT-3 a Good Data Annotator?](https://mlsanity.com/paper/2212.10450/) (2022)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [Why do language models perform worse for morphologically complex languages?](https://mlsanity.com/paper/2411.14198/) (2024)
- [Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical Investigation](https://mlsanity.com/paper/2008.06788/) (2020)
- [Measuring Annotator Agreement Generally across Complex Structured, Multi-object, and Free-text Annotation Tasks](https://mlsanity.com/paper/2212.09503/) (2022)
- [UDPipe at EvaLatin 2020: Contextualized Embeddings and Treebank Embeddings](https://mlsanity.com/paper/2006.03687/) (2020)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages](https://mlsanity.com/paper/2506.07274/) (2025)
- [Enhancing Korean Dependency Parsing with Morphosyntactic Features](https://mlsanity.com/paper/2503.21029/) (2025)
- [Foundations and Evaluations in NLP](https://mlsanity.com/paper/2504.01342/) (2025)

## Abstract (arXiv)

Developing effective educational technologies for low-resource agglutinative languages like Uyghur is often hindered by the mismatch between existing annotation frameworks and specific grammatical structures. To address this challenge, this study introduces the Modern Uyghur Dependency Treebank (MUDT), a linguistically grounded annotation framework specifically designed to capture the agglutinative complexity of Uyghur, including zero copula constructions and fine-grained case marking. Utilizing a hybrid pipeline that combines Large Language Model pre-annotation with rigorous human correction, a high-quality treebank consisting of 3,456 sentences was constructed. Intrinsic structural evaluation reveals that MUDT significantly improves dependency projectivity by reducing the crossing-arc rate from 7.35% in the Universal Dependencies standard to 0.06%. Extrinsic parsing experiments using UDPipe and Stanza further demonstrate that models trained on MUDT achieve superior in-domain accuracy and cross-domain generalization compared to UD-based baselines. To validate the practical utility of this computational resource, an AI-assisted grammar tutoring system was developed to translate MUDT-based syntactic analyses into interpretable pedagogical feedback. A controlled experiment involving 35 second-language learners indicated that students receiving syntax-aware feedback achieved significantly higher learning gains compared to those in a control group. These findings establish MUDT as a robust foundation for syntactic analysis and underscore the critical role of linguistically informed natural language processing resources in bridging the gap between computational models and the cognitive needs of second-language learners.