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# Allen Institute for AI @ SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages

**Authors:** Lester James V. Miranda · **Year:** 2024 · **Venue:** Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP (SIGTYP 2024) **Link:** [aclanthology.org/2024.sigtyp-1.18](https://aclanthology.org/2024.sigtyp-1.18/) · library `ud-2024-miranda-allen-institute-ai-sigtyp-2024` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**0** citations](https://openalex.org/works?filter=cites:W7126368208) openalex-snapshot · 2026-10-14[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/ud-2024-miranda-allen-institute-ai-sigtyp-2024/) of the 140673 papers whose bibliographies we read[Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP](https://aclanthology.org/volumes/2024.sigtyp-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2024.sigtyp-1.18/)
- [PDF](https://aclanthology.org/2024.sigtyp-1.18.pdf)
- [DOI 10.18653/v1/2024.sigtyp-1.18](https://doi.org/10.18653/v1/2024.sigtyp-1.18)
- [OpenAlex](https://openalex.org/W7126368208)

**Authors (1)**

1. [Miranda, Lester James V.](https://openalex.org/A5133583162) [Semantic Scholar](https://www.semanticscholar.org/author/13614871) · h 0 · 0 citations · 3 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

The author describes the constrained-track entry for the SIGTYP 2024 shared task, which asks for POS tags, morphological features and lemmas for thirteen historical languages, using only the organisers' UD v2.12 data (about 2.6M training tokens). The system pretrains a 126M-parameter RoBERTa-style multilingual model, LiBERTus, on the shared-task tokens, and then fine-tunes a spaCy pipeline per language for tagging, morphology and lemmatisation. On the validation set most language-task pairs reach F1 of at least 0.70; the best averages are Medieval Latin (0.968) and the worst is Ancient Hebrew (0.468). The leaderboard scores for Ancient Hebrew and Coptic are poor, because the models were trained on whole multiword tokens and never saw the subtokens that the test script scores. Cross-lingual tests show that a model transfers to another language for tagging and lemmatisation, but not for morphology.

## Ideas

- I1. Score the Hebrew segmentation separately from the tagging: report subtoken F1 (prefix, article, suffix) and token-level tag F1 as two numbers on the held-out Hebrew data. The paper's failure shows that these two errors are hidden in one score, and Mova's `tools/hbo` needs them apart.
- I2. Count unseen feature combinations: the paper treats each whole combination of morphological features as one class, so it can only predict combinations present in training. A measure on held-out Hebrew data of how many gold feature bundles are unseen in training tells us how far a table-based or compositional tagger is needed.
- I3. Lemmatisation transfer between related languages (the paper finds Classical Latin and Medieval Latin, and Old Church Slavonic and Old East Slavic, work across each other). A test that checks Hebrew and Aramaic lemma pairs through the shared lexicon, as a measure of coverage, before any transfer is attempted.

## For Mova

I1 is a measure for `tools/hbo` and belongs in [13. Biblical Hebrew in Mova: the tools](https://md.mova.elamur.ai/free/en/docs/languages/hbo/13-mova-tools/) with the test sets. I2 is a diagnostic for the morphology tags across [The Noun, the Adjective and the Numeral](https://md.mova.elamur.ai/free/en/docs/languages/hbo/04-noun/) and `06-verb.md`. I3 is a test for [The lexicon of Biblical Aramaic](https://md.mova.elamur.ai/free/en/docs/languages/arc/lexicon/) and [Aramaic in Mova: what it would need](https://md.mova.elamur.ai/free/en/docs/languages/arc/mova/). The neural model itself is not used; the paper's measures are.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2024.sigtyp-1.18](https://doi.org/10.18653/v1/2024.sigtyp-1.18)

```
@inproceedings{miranda2024allen,
  title = {{Allen Institute for AI @ SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages}},
  author = {Miranda, Lester James V.},
  booktitle = {Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP},
  year = {2024},
  doi = {10.18653/v1/2024.sigtyp-1.18},
  url = {https://doi.org/10.18653/v1/2024.sigtyp-1.18}
}
```

- [← previous A Universal Dependencies Treebank of Ancient Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/ud-2022-swanson-universal-dependencies-treebank-ancient-hebrew/)
- [next → Producing a Parallel Universal Dependencies Treebank of Ancient Hebrew and Ancient Greek via Cross-Lingual Projection](https://md.mova.elamur.ai/free/en/docs/papers/ud-2024-swanson-producing-parallel-universal-dependencies-treebank/)
- [↑ back to Ancient Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo/)

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