@inproceedings{sharma-etal-2020-memebusters,
title = "Memebusters at {S}em{E}val-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning",
author = "Sharma, Mayukh and
Kandasamy, Ilanthenral and
Vasantha, W.b.",
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-1.154",
doi = "10.18653/v1/2020.semeval-1.154",
pages = "1163--1171",
abstract = "In this paper, we describe our deep learning system used for SemEval 2020 Task 8: Memotion analysis. We participated in all the subtasks i.e Subtask A: Sentiment classification, Subtask B: Humor classification, and Subtask C: Scales of semantic classes. Similar multimodal architecture was used for each subtask. The proposed architecture makes use of transfer learning for images and text feature extraction. The extracted features are then fused together using stacked bidirectional Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention mechanism for final predictions. We also propose a single model for predicting semantic classes (Subtask B) as well as their scales (Subtask C) by branching the final output of the post LSTM dense layers. Our model was ranked 5 in Subtask B and ranked 8 in Subtask C and performed nicely in Subtask A on the leader board. Our system makes use of transfer learning for feature extraction and fusion of image and text features for predictions.",
}
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<abstract>In this paper, we describe our deep learning system used for SemEval 2020 Task 8: Memotion analysis. We participated in all the subtasks i.e Subtask A: Sentiment classification, Subtask B: Humor classification, and Subtask C: Scales of semantic classes. Similar multimodal architecture was used for each subtask. The proposed architecture makes use of transfer learning for images and text feature extraction. The extracted features are then fused together using stacked bidirectional Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention mechanism for final predictions. We also propose a single model for predicting semantic classes (Subtask B) as well as their scales (Subtask C) by branching the final output of the post LSTM dense layers. Our model was ranked 5 in Subtask B and ranked 8 in Subtask C and performed nicely in Subtask A on the leader board. Our system makes use of transfer learning for feature extraction and fusion of image and text features for predictions.</abstract>
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%0 Conference Proceedings
%T Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning
%A Sharma, Mayukh
%A Kandasamy, Ilanthenral
%A Vasantha, W.b.
%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 sharma-etal-2020-memebusters
%X In this paper, we describe our deep learning system used for SemEval 2020 Task 8: Memotion analysis. We participated in all the subtasks i.e Subtask A: Sentiment classification, Subtask B: Humor classification, and Subtask C: Scales of semantic classes. Similar multimodal architecture was used for each subtask. The proposed architecture makes use of transfer learning for images and text feature extraction. The extracted features are then fused together using stacked bidirectional Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention mechanism for final predictions. We also propose a single model for predicting semantic classes (Subtask B) as well as their scales (Subtask C) by branching the final output of the post LSTM dense layers. Our model was ranked 5 in Subtask B and ranked 8 in Subtask C and performed nicely in Subtask A on the leader board. Our system makes use of transfer learning for feature extraction and fusion of image and text features for predictions.
%R 10.18653/v1/2020.semeval-1.154
%U https://aclanthology.org/2020.semeval-1.154
%U https://doi.org/10.18653/v1/2020.semeval-1.154
%P 1163-1171
Markdown (Informal)
[Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning](https://aclanthology.org/2020.semeval-1.154) (Sharma et al., SemEval 2020)
ACL