Fine-tuning DeBERTa-v3 to Automate Spatial Language Classification

Sam Agnoli; Qingzhou Shi; David Uttal

Fine-tuning DeBERTa-v3 to Automate Spatial Language Classification

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Abstract

To automate spatial language classification, we fine-tuned DeBERTa-v3, a transformer-based language model, using a 33,284-word dataset following a 70:15:15 train-validation-test split. The model performed well in a held-out test comparing performance to human coders (Cohen’s kappa = .88).

Anthology ID:: 2026.aimecon-wip.28
Volume:: Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Month:: October
Year:: 2026
Address:: Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
Editors:: Joshua Wilson, Christopher Ormerod, Magdalen Beiting-Parrish
Venue:: AIME-Con
SIG:
Publisher:: National Council on Measurement in Education (NCME)
Note:
Pages:: 211–215
Language:
URL:: https://aclanthology.org/2026.aimecon-wip.28/
DOI:
Bibkey:
Cite (ACL):: Sam Agnoli, Qingzhou Shi, and David Uttal. 2026. Fine-tuning DeBERTa-v3 to Automate Spatial Language Classification. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 211–215, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Fine-tuning DeBERTa-v3 to Automate Spatial Language Classification (Agnoli et al., AIME-Con 2026)
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PDF:: https://aclanthology.org/2026.aimecon-wip.28.pdf

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@inproceedings{agnoli-etal-2026-fine,
    title = "Fine-tuning {D}e{BERT}a-v3 to Automate Spatial Language Classification",
    author = "Agnoli, Sam  and
      Shi, Qingzhou  and
      Uttal, David",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting-Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Works in Progress",
    month = oct,
    year = "2026",
    address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://aclanthology.org/2026.aimecon-wip.28/",
    pages = "211--215",
    ISBN = "979-8-9983004-1-7",
    abstract = "To automate spatial language classification, we fine-tuned DeBERTa-v3, a transformer-based language model, using a 33,284-word dataset following a 70:15:15 train-validation-test split. The model performed well in a held-out test comparing performance to human coders (Cohen{'}s kappa = .88)."
}

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%0 Conference Proceedings
%T Fine-tuning DeBERTa-v3 to Automate Spatial Language Classification
%A Agnoli, Sam
%A Shi, Qingzhou
%A Uttal, David
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-1-7
%F agnoli-etal-2026-fine
%X To automate spatial language classification, we fine-tuned DeBERTa-v3, a transformer-based language model, using a 33,284-word dataset following a 70:15:15 train-validation-test split. The model performed well in a held-out test comparing performance to human coders (Cohen’s kappa = .88).
%U https://aclanthology.org/2026.aimecon-wip.28/
%P 211-215

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Markdown (Informal)

[Fine-tuning DeBERTa-v3 to Automate Spatial Language Classification](https://aclanthology.org/2026.aimecon-wip.28/) (Agnoli et al., AIME-Con 2026)

Fine-tuning DeBERTa-v3 to Automate Spatial Language Classification (Agnoli et al., AIME-Con 2026)

ACL

Sam Agnoli, Qingzhou Shi, and David Uttal. 2026. Fine-tuning DeBERTa-v3 to Automate Spatial Language Classification. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 211–215, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).