@inproceedings{de-la-pena-sarracen-etal-2020-prhlt,
title = "{PRHLT}-{UPV} at {S}em{E}val-2020 Task 8: Study of Multimodal Techniques for Memes Analysis",
author = "De la Pe{\~n}a Sarrac{\'e}n, Gretel Liz and
Rosso, Paolo and
Giachanou, Anastasia",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.115",
doi = "10.18653/v1/2020.semeval-1.115",
pages = "908--915",
abstract = "This paper describes the system submitted by the PRHLT-UPV team for the task 8 of SemEval-2020: Memotion Analysis. We propose a multimodal model that combines pretrained models of the BERT and VGG architectures. The BERT model is used to process the textual information and VGG the images. The multimodal model is used to classify memes according to the presence of offensive, sarcastic, humorous and motivating content. Also, a sentiment analysis of memes is carried out with the proposed model. In the experiments, the model is compared with other approaches to analyze the relevance of the multimodal model. The results show encouraging performances on the final leaderboard of the competition, reaching good positions in the ranking of systems.",
}
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<abstract>This paper describes the system submitted by the PRHLT-UPV team for the task 8 of SemEval-2020: Memotion Analysis. We propose a multimodal model that combines pretrained models of the BERT and VGG architectures. The BERT model is used to process the textual information and VGG the images. The multimodal model is used to classify memes according to the presence of offensive, sarcastic, humorous and motivating content. Also, a sentiment analysis of memes is carried out with the proposed model. In the experiments, the model is compared with other approaches to analyze the relevance of the multimodal model. The results show encouraging performances on the final leaderboard of the competition, reaching good positions in the ranking of systems.</abstract>
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%0 Conference Proceedings
%T PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis
%A De la Peña Sarracén, Gretel Liz
%A Rosso, Paolo
%A Giachanou, Anastasia
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F de-la-pena-sarracen-etal-2020-prhlt
%X This paper describes the system submitted by the PRHLT-UPV team for the task 8 of SemEval-2020: Memotion Analysis. We propose a multimodal model that combines pretrained models of the BERT and VGG architectures. The BERT model is used to process the textual information and VGG the images. The multimodal model is used to classify memes according to the presence of offensive, sarcastic, humorous and motivating content. Also, a sentiment analysis of memes is carried out with the proposed model. In the experiments, the model is compared with other approaches to analyze the relevance of the multimodal model. The results show encouraging performances on the final leaderboard of the competition, reaching good positions in the ranking of systems.
%R 10.18653/v1/2020.semeval-1.115
%U https://aclanthology.org/2020.semeval-1.115
%U https://doi.org/10.18653/v1/2020.semeval-1.115
%P 908-915
Markdown (Informal)
[PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis](https://aclanthology.org/2020.semeval-1.115) (De la Peña Sarracén et al., SemEval 2020)
ACL