@inproceedings{lai-etal-2024-encoding,
    title = "Encoding Gesture in Multimodal Dialogue: Creating a Corpus of Multimodal {AMR}",
    author = "Lai, Kenneth  and
      Brutti, Richard  and
      Donatelli, Lucia  and
      Pustejovsky, James",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.515",
    pages = "5806--5818",
    abstract = "Abstract Meaning Representation (AMR) is a general-purpose meaning representation that has become popular for its clear structure, ease of annotation and available corpora, and overall expressiveness. While AMR was designed to represent sentence meaning in English text, recent research has explored its adaptation to broader domains, including documents, dialogues, spatial information, cross-lingual tasks, and gesture. In this paper, we present an annotated corpus of multimodal (speech and gesture) AMR in a task-based setting. Our corpus is multilayered, containing temporal alignments to both the speech signal and to descriptions of gesture morphology. We also capture coreference relationships across modalities, enabling fine-grained analysis of how the semantics of gesture and natural language interact. We discuss challenges that arise when identifying cross-modal coreference and anaphora, as well as in creating and evaluating multimodal corpora in general. Although we find AMR{'}s abstraction away from surface form (in both language and gesture) occasionally too coarse-grained to capture certain cross-modal interactions, we believe its flexibility allows for future work to fill in these gaps. Our corpus and annotation guidelines are available at https://github.com/klai12/encoding-gesture-multimodal-dialogue.",
}

Abstract Meaning Representation (AMR) is a general-purpose meaning representation that has become popular for its clear structure, ease of annotation and available corpora, and overall expressiveness. While AMR was designed to represent sentence meaning in English text, recent research has explored its adaptation to broader domains, including documents, dialogues, spatial information, cross-lingual tasks, and gesture. In this paper, we present an annotated corpus of multimodal (speech and gesture) AMR in a task-based setting. Our corpus is multilayered, containing temporal alignments to both the speech signal and to descriptions of gesture morphology. We also capture coreference relationships across modalities, enabling fine-grained analysis of how the semantics of gesture and natural language interact. We discuss challenges that arise when identifying cross-modal coreference and anaphora, as well as in creating and evaluating multimodal corpora in general. Although we find AMR{'}s abstraction away from surface form (in both language and gesture) occasionally too coarse-grained to capture certain cross-modal interactions, we believe its flexibility allows for future work to fill in these gaps. Our corpus and annotation guidelines are available at https://github.com/klai12/encoding-gesture-multimodal-dialogue.
Lai, Kenneth Brutti, Richard Donatelli, Lucia Pustejovsky, James
Encoding Gesture in Multimodal Dialogue: Creating a Corpus of Multimodal AMR