# Latin Treebanks in Review: An Evaluation of Morphological Tagging Across Time

arXiv: https://arxiv.org/abs/2408.06675 · HTML: https://mlsanity.com/paper/2408.06675/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words](https://mlsanity.com/paper/2101.00403/) (2021)
- [Latin BERT: A Contextual Language Model for Classical Philology](https://mlsanity.com/paper/2009.10053/) (2020)
- [Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing](https://mlsanity.com/paper/1908.07448/) (2019)
- [UDPipe at EvaLatin 2020: Contextualized Embeddings and Treebank Embeddings](https://mlsanity.com/paper/2006.03687/) (2020)
- [LatinCy: Synthetic Trained Pipelines for Latin NLP](https://mlsanity.com/paper/2305.04365/) (2023)

## Built on by (most cited first, 1 of 1)

- [Contextual morphologically-guided tokenization for Latin encoder models](https://mlsanity.com/paper/2511.09709/) (2025)

## Abstract (arXiv)

Existing Latin treebanks draw from Latin's long written tradition, spanning 17 centuries and a variety of cultures. Recent efforts have begun to harmonize these treebanks' annotations to better train and evaluate morphological taggers. However, the heterogeneity of these treebanks must be carefully considered to build effective and reliable data. In this work, we review existing Latin treebanks to identify the texts they draw from, identify their overlap, and document their coverage across time and genre. We additionally design automated conversions of their morphological feature annotations into the conventions of standard Latin grammar. From this, we build new time-period data splits that draw from the existing treebanks which we use to perform a broad cross-time analysis for POS and morphological feature tagging. We find that BERT-based taggers outperform existing taggers while also being more robust to cross-domain shifts.