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        <title>German Counseling Grounding-Act Corpus (GRACO)</title>
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            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
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            <namePart type="given">Henk</namePart>
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    <abstract>We present a corpus of 196 German counseling conversations (ca. 25k turns) between advice seekers and counselors from nine domains. A subset of 11.5k turns was double-annotated with grounding acts (e.g., acknowledgments, repairs), attempts to advance the conversation, success of advancing, and conversation phases. Baseline classification experiments with logistic regression and GBERT-base illustrate the impact of class imbalance in grounding-act classification. For logistic regression, train-only balancing improves Macro-F1 from 0.417 [0.377–0.434] to 0.444 [0.394–0.478]. For GBERT-base, performance remains competitive (Macro-F1 0.481), with balancing yielding comparable results under the same evaluation protocol. Given the scarcity of German corpora of naturally occurring conversations annotated for grounding phenomena, we provide a novel resource for both conversation analysis and natural language processing, facilitating the design of realistic human-language model interactions in German. Code and data are available at https://osf.io/6k275/overview.</abstract>
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%0 Conference Proceedings
%T German Counseling Grounding-Act Corpus (GRACO)
%A Belosevic, Milena
%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 belosevic-2026-german
%X We present a corpus of 196 German counseling conversations (ca. 25k turns) between advice seekers and counselors from nine domains. A subset of 11.5k turns was double-annotated with grounding acts (e.g., acknowledgments, repairs), attempts to advance the conversation, success of advancing, and conversation phases. Baseline classification experiments with logistic regression and GBERT-base illustrate the impact of class imbalance in grounding-act classification. For logistic regression, train-only balancing improves Macro-F1 from 0.417 [0.377–0.434] to 0.444 [0.394–0.478]. For GBERT-base, performance remains competitive (Macro-F1 0.481), with balancing yielding comparable results under the same evaluation protocol. Given the scarcity of German corpora of naturally occurring conversations annotated for grounding phenomena, we provide a novel resource for both conversation analysis and natural language processing, facilitating the design of realistic human-language model interactions in German. Code and data are available at https://osf.io/6k275/overview.
%R 10.63317/3cbc5jpg7spd
%U https://aclanthology.org/2026.lrec-1.495/
%U https://doi.org/10.63317/3cbc5jpg7spd
%P 6251-6261

Markdown (Informal)

[German Counseling Grounding-Act Corpus (GRACO)](https://aclanthology.org/2026.lrec-1.495/) (Belosevic, LREC 2026)

• German Counseling Grounding-Act Corpus (GRACO) (Belosevic, LREC 2026)

ACL

• Milena Belosevic. 2026. German Counseling Grounding-Act Corpus (GRACO). In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6251–6261, Palma de Mallorca, Spain. ELRA Language Resource Association.