GermDetect: Verb Placement Error Detection Datasets for Learners of Germanic Languages

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A4WGXVVW8" target="_blank" >RIV/00216208:11320/26:4WGXVVW8 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2025.bea-1.59/" target="_blank" >https://aclanthology.org/2025.bea-1.59/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2025.bea-1.59" target="_blank" >10.18653/v1/2025.bea-1.59</a>

Result language

angličtina

Original language name

GermDetect: Verb Placement Error Detection Datasets for Learners of Germanic Languages

Original language description

Correct verb placement is difficult to acquire for second-language (L2) learners of Germanic languages. However, word order errors and, consequently, verb placement errors, are heavily underrepresented in benchmark datasets of NLP tasks such as grammatical error detection (GED)/correction (GEC) and linguistic acceptability assessment (LA). If they are present, they are most often naively introduced, or classification occurs at the sentence level, preventing the precise identification of individual errors and the provision of appropriate feedback to learners. To remedy this, we present GermDetect: Universal Dependencies-based (UD), linguistically informed verb placement error detection datasets for learners of Germanic languages, designed as a token classification task. As our datasets are UD-based, we are able to provide them in most major Germanic languages: Afrikaans, German, Dutch, Faroese, Icelandic, Danish, Norwegian (Bokmål and Nynorsk), and Swedish. We train multilingual BERT (mBERT) models on GermDetect and show that linguistically informed, UD-based error induction results in more effective models for verb placement error detection than models trained on naively introduced errors. Finally, we conduct ablation studies on multilingual training and find that lower-resource languages benefit from the inclusion of structurally related languages in training.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications

ISBN

979-8-89176-270-1

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

818-829

Publisher name

Association for Computational Linguistics

Place of publication

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Event location

Vienna, Austria

Event date

Jan 1, 2026

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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