@inproceedings{montariol-etal-2022-fine,
title = "Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance",
author = "Montariol, Syrielle and
Simon, {\'E}tienne and
Riabi, Arij and
Seddah, Djam{\'e}",
editor = "Chakraborty, Tanmoy and
Akhtar, Md. Shad and
Shu, Kai and
Bernard, H. Russell and
Liakata, Maria and
Nakov, Preslav and
Srivastava, Aseem",
booktitle = "Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.constraint-1.7",
doi = "10.18653/v1/2022.constraint-1.7",
pages = "55--65",
abstract = "We propose our solution to the multimodal semantic role labeling task from the CONSTRAINT{'}22 workshop. The task aims at classifying entities in memes into classes such as {``}hero{''} and {``}villain{''}. We use several pre-trained multi-modal models to jointly encode the text and image of the memes, and implement three systems to classify the role of the entities. We propose dynamic sampling strategies to tackle the issue of class imbalance. Finally, we perform qualitative analysis on the representations of the entities.",
}
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<abstract>We propose our solution to the multimodal semantic role labeling task from the CONSTRAINT’22 workshop. The task aims at classifying entities in memes into classes such as “hero” and “villain”. We use several pre-trained multi-modal models to jointly encode the text and image of the memes, and implement three systems to classify the role of the entities. We propose dynamic sampling strategies to tackle the issue of class imbalance. Finally, we perform qualitative analysis on the representations of the entities.</abstract>
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%0 Conference Proceedings
%T Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance
%A Montariol, Syrielle
%A Simon, Étienne
%A Riabi, Arij
%A Seddah, Djamé
%Y Chakraborty, Tanmoy
%Y Akhtar, Md. Shad
%Y Shu, Kai
%Y Bernard, H. Russell
%Y Liakata, Maria
%Y Nakov, Preslav
%Y Srivastava, Aseem
%S Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F montariol-etal-2022-fine
%X We propose our solution to the multimodal semantic role labeling task from the CONSTRAINT’22 workshop. The task aims at classifying entities in memes into classes such as “hero” and “villain”. We use several pre-trained multi-modal models to jointly encode the text and image of the memes, and implement three systems to classify the role of the entities. We propose dynamic sampling strategies to tackle the issue of class imbalance. Finally, we perform qualitative analysis on the representations of the entities.
%R 10.18653/v1/2022.constraint-1.7
%U https://aclanthology.org/2022.constraint-1.7
%U https://doi.org/10.18653/v1/2022.constraint-1.7
%P 55-65
Markdown (Informal)
[Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance](https://aclanthology.org/2022.constraint-1.7) (Montariol et al., CONSTRAINT 2022)
ACL