@inproceedings{jamil-etal-2025-graph,
    title = "A Graph Autoencoder Approach for Gesture Classification with Gesture {AMR}",
    author = "Jamil, Huma  and
      Khebour, Ibrahim  and
      Lai, Kenneth  and
      Pustejovsky, James  and
      Krishnaswamy, Nikhil",
    editor = "Evang, Kilian  and
      Kallmeyer, Laura  and
      Pogodalla, Sylvain",
    booktitle = "Proceedings of the 16th International Conference on Computational Semantics",
    month = sep,
    year = "2025",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/2025.iwcs-1.5/",
    pages = "51--58",
    ISBN = "979-8-89176-316-6",
    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."
}