mlsanityData to 14 Oct 2026

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

arXiv 2408.06675 ↗First posted 13 Aug 2024cs.CLcited by 1

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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.

Key facts. Cited by 1 paper 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

9 of 40 references.

References · 9

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  3. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
  4. 2018Learning Word Vectors for 157 Languagescited by 280
  5. 2021Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Wordscited by 63
  6. 2020Latin BERT: A Contextual Language Model for Classical Philologycited by 43
  7. 2019Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsingcited by 8
  8. 2020UDPipe at EvaLatin 2020: Contextualized Embeddings and Treebank Embeddingscited by 6
  9. 2023LatinCy: Synthetic Trained Pipelines for Latin NLPcited by 5

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