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        <title>A Graph Autoencoder Approach for Gesture Classification with Gesture AMR</title>
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    <abstract>We present a novel graph autoencoder (GAE) architecture for classifying gestures using Gesture Abstract Meaning Representation (GAMR), a structured semantic annotation framework for gestures in collaborative tasks. We leverage the inherent graphical structure of GAMR by employing Graph Neural Networks (GNNs), specifically an Edge-aware Graph Attention Network (EdgeGAT), to learn embeddings of gesture semantic representations. Using the EGGNOG dataset, which captures diverse physical gesture forms expressing similar semantics, we evaluate our GAE on a multi-label classification task for gestural actions. Results indicate that our approach significantly outperforms naive baselines and is competitive with specialized Transformer-based models like AMRBART, despite using considerably fewer parameters and no pretraining. This work highlights the effectiveness of structured graphical representations in modeling multimodal semantics, offering a scalable and efficient approach to gesture interpretation in situated human-agent collaborative scenarios.</abstract>
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%0 Conference Proceedings
%T A Graph Autoencoder Approach for Gesture Classification with Gesture AMR
%A Jamil, Huma
%A Khebour, Ibrahim
%A Lai, Kenneth
%A Pustejovsky, James
%A Krishnaswamy, Nikhil
%Y Evang, Kilian
%Y Kallmeyer, Laura
%Y Pogodalla, Sylvain
%S Proceedings of the 16th International Conference on Computational Semantics
%D 2025
%8 September
%I Association for Computational Linguistics
%C Düsseldorf, Germany
%@ 979-8-89176-316-6
%F jamil-etal-2025-graph
%X We present a novel graph autoencoder (GAE) architecture for classifying gestures using Gesture Abstract Meaning Representation (GAMR), a structured semantic annotation framework for gestures in collaborative tasks. We leverage the inherent graphical structure of GAMR by employing Graph Neural Networks (GNNs), specifically an Edge-aware Graph Attention Network (EdgeGAT), to learn embeddings of gesture semantic representations. Using the EGGNOG dataset, which captures diverse physical gesture forms expressing similar semantics, we evaluate our GAE on a multi-label classification task for gestural actions. Results indicate that our approach significantly outperforms naive baselines and is competitive with specialized Transformer-based models like AMRBART, despite using considerably fewer parameters and no pretraining. This work highlights the effectiveness of structured graphical representations in modeling multimodal semantics, offering a scalable and efficient approach to gesture interpretation in situated human-agent collaborative scenarios.
%U https://aclanthology.org/2025.iwcs-main.4/
%P 41-48

Markdown (Informal)

[A Graph Autoencoder Approach for Gesture Classification with Gesture AMR](https://aclanthology.org/2025.iwcs-main.4/) (Jamil et al., IWCS 2025)

• A Graph Autoencoder Approach for Gesture Classification with Gesture AMR (Jamil et al., IWCS 2025)

ACL

• Huma Jamil, Ibrahim Khebour, Kenneth Lai, James Pustejovsky, and Nikhil Krishnaswamy. 2025. A Graph Autoencoder Approach for Gesture Classification with Gesture AMR. In Proceedings of the 16th International Conference on Computational Semantics, pages 41–48, Düsseldorf, Germany. Association for Computational Linguistics.