Natalia Sigiliano


2024

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Frame2: A FrameNet-based Multimodal Dataset for Tackling Text-image Interactions in Video
Frederico Belcavello | Tiago Timponi Torrent | Ely E. Matos | Adriana S. Pagano | Maucha Gamonal | Natalia Sigiliano | Lívia Vicente Dutra | Helen de Andrade Abreu | Mairon Samagaio | Mariane Carvalho | Franciany Campos | Gabrielly Azalim | Bruna Mazzei | Mateus Fonseca de Oliveira | Ana Carolina Loçasso Luz | Lívia Pádua Ruiz | Júlia Bellei | Amanda Pestana | Josiane Costa | Iasmin Rabelo | Anna Beatriz Silva | Raquel Roza | Mariana Souza | Igor Oliveira
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

This paper presents the Frame2 dataset, a multimodal dataset built from a corpus of a Brazilian travel TV show annotated for FrameNet categories for both the text and image communicative modes. Frame2 comprises 230 minutes of video, which are correlated with 2,915 sentences either transcribing the audio spoken during the episodes or the subtitling segments of the show where the host conducts interviews in English. For this first release of the dataset, a total of 11,796 annotation sets for the sentences and 6,841 for the video are included. Each of the former includes a target lexical unit evoking a frame or one or more frame elements. For each video annotation, a bounding box in the image is correlated with a frame, a frame element and lexical unit evoking a frame in FrameNet.