# Second language Korean Universal Dependency treebank v1.2: Focus on data augmentation and annotation scheme refinement

arXiv: https://arxiv.org/abs/2503.14718 · HTML: https://mlsanity.com/paper/2503.14718/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)
- [What to do about non-standard (or non-canonical) language in NLP](https://mlsanity.com/paper/1608.07836/) (2016)
- [Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples](https://mlsanity.com/paper/1805.06556/) (2018)
- [Universal Dependencies for Learner English](https://mlsanity.com/paper/1605.04278/) (2016)

## Built on by (most cited first, 3 of 3)

- [Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages](https://mlsanity.com/paper/2506.07274/) (2025)
- [UD-KSL Treebank v1.3: A semi-automated framework for aligning XPOS-extracted units with UPOS tags](https://mlsanity.com/paper/2506.09009/) (2025)
- [Parser agreement and disagreement in L2 Korean UD: Implications for human-in-the-loop annotation](https://mlsanity.com/paper/2605.06625/) (2026)

## Abstract (arXiv)

We expand the second language (L2) Korean Universal Dependencies (UD) treebank with 5,454 manually annotated sentences. The annotation guidelines are also revised to better align with the UD framework. Using this enhanced treebank, we fine-tune three Korean language models and evaluate their performance on in-domain and out-of-domain L2-Korean datasets. The results show that fine-tuning significantly improves their performance across various metrics, thus highlighting the importance of using well-tailored L2 datasets for fine-tuning first-language-based, general-purpose language models for the morphosyntactic analysis of L2 data.