ArchiMeDe@ DANKMEMES: A New Model Architecture for Meme Detection

Jinen Setpal*, Gabriele Sarti

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

Samenvatting

We introduce ArchiMeDe, a multimodal neural network-based architecture used to solve the DANKMEMES meme detections subtask at the 2020 EVALITA campaign. The system incorporates information from visual and textual sources through a multimodal neural ensemble to predict if input images and their respective metadata are memes or not. Each pretrained neural network in the ensemble is first fine-tuned individually on the training dataset to perform domain adaptation. Learned text and visual representations are then concatenated to obtain a single multimodal embedding, and the final prediction is performed through majority voting by all networks in the ensemble.
Originele taal-2English
TitelProceedings of the Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2020)
RedacteurenValerio Basile, Danilo Croce, Maria Di Maro, Lucia Passaro
UitgeverijCEUR Workshop Proceedings (CEUR-WS.org)
StatusPublished - 17-dec.-2020
Extern gepubliceerdJa
EvenementEvaluation Campaign of Natural Language Processing and Speech Tools for Italian - Online
Duur: 17-dec.-2020 → …
Congresnummer: 7

Workshop

WorkshopEvaluation Campaign of Natural Language Processing and Speech Tools for Italian
Verkorte titelEVALITA 2020
Periode17/12/2020 → …

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