@inproceedings{wartena-etal-2026-cross,
title = "Cross-Lingual and Cross-Cultural Transfer of Talk Move Classification to {G}erman Science Classrooms",
author = "Wartena, Christian and
Schumburg, Christian and
Nehring, Andreas and
Ebert, Marcel and
Korneck, Friederike and
Schmitt, David and
Irmer, Marie and
Neuhaus, Birgit",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.156/",
doi = "10.63317/3nynifnswvtk",
pages = "1989--1997",
abstract = "Talk moves are discourse categories used to analyse classroom interactions. They provide insights into the types of exchanges between teachers and students and can serve as indicators of teaching quality, supporting feedback and reflection. The automatic classification of talk moves is therefore valuable for educational research and teacher development. While previous studies have explored this task, almost all have focused on English data. We constructed a small corpus of German science classroom transcripts and investigated whether multilingual language models can classify talk moves effectively under data-scarce conditions. Specifically, we examined (1) training with a very limited amount of German data and (2) cross-lingual transfer from English training data, which also entails cross-cultural adaptation. Our results show that multilingual large language models are capable of cross-lingual and cross-cultural transfer, but models trained directly on even a small amount of German data achieve better performance. Combining English and German data yields the best results overall, though the additional benefit of including English data is small."
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<abstract>Talk moves are discourse categories used to analyse classroom interactions. They provide insights into the types of exchanges between teachers and students and can serve as indicators of teaching quality, supporting feedback and reflection. The automatic classification of talk moves is therefore valuable for educational research and teacher development. While previous studies have explored this task, almost all have focused on English data. We constructed a small corpus of German science classroom transcripts and investigated whether multilingual language models can classify talk moves effectively under data-scarce conditions. Specifically, we examined (1) training with a very limited amount of German data and (2) cross-lingual transfer from English training data, which also entails cross-cultural adaptation. Our results show that multilingual large language models are capable of cross-lingual and cross-cultural transfer, but models trained directly on even a small amount of German data achieve better performance. Combining English and German data yields the best results overall, though the additional benefit of including English data is small.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual and Cross-Cultural Transfer of Talk Move Classification to German Science Classrooms
%A Wartena, Christian
%A Schumburg, Christian
%A Nehring, Andreas
%A Ebert, Marcel
%A Korneck, Friederike
%A Schmitt, David
%A Irmer, Marie
%A Neuhaus, Birgit
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F wartena-etal-2026-cross
%X Talk moves are discourse categories used to analyse classroom interactions. They provide insights into the types of exchanges between teachers and students and can serve as indicators of teaching quality, supporting feedback and reflection. The automatic classification of talk moves is therefore valuable for educational research and teacher development. While previous studies have explored this task, almost all have focused on English data. We constructed a small corpus of German science classroom transcripts and investigated whether multilingual language models can classify talk moves effectively under data-scarce conditions. Specifically, we examined (1) training with a very limited amount of German data and (2) cross-lingual transfer from English training data, which also entails cross-cultural adaptation. Our results show that multilingual large language models are capable of cross-lingual and cross-cultural transfer, but models trained directly on even a small amount of German data achieve better performance. Combining English and German data yields the best results overall, though the additional benefit of including English data is small.
%R 10.63317/3nynifnswvtk
%U https://aclanthology.org/2026.lrec-1.156/
%U https://doi.org/10.63317/3nynifnswvtk
%P 1989-1997
Markdown (Informal)
[Cross-Lingual and Cross-Cultural Transfer of Talk Move Classification to German Science Classrooms](https://aclanthology.org/2026.lrec-1.156/) (Wartena et al., LREC 2026)
ACL
- Christian Wartena, Christian Schumburg, Andreas Nehring, Marcel Ebert, Friederike Korneck, David Schmitt, Marie Irmer, and Birgit Neuhaus. 2026. Cross-Lingual and Cross-Cultural Transfer of Talk Move Classification to German Science Classrooms. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1989–1997, Palma de Mallorca, Spain. ELRA Language Resource Association.