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LT3 at SemEval-2020 Task 8 : multi-modal multi-task learning for memotion analysis

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Abstract
Internet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging research object for automatic analysis. In this paper, we describe our contribution to the SemEval2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which incorporates “memebeddings”, viz. joint text and vision features, to learn and optimize for all three Memotion subtasks simultaneously. The experimental results show that the proposed system constantly outperforms the competition’s baseline, and the system setup with continual learning (where tasks are trained sequentially) obtains the best classification F1-scores.
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LT3

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MLA
Singh, Pranaydeep, et al. “LT3 at SemEval-2020 Task 8 : Multi-Modal Multi-Task Learning for Memotion Analysis.” Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), Association for Computational Linguistics (ACL), 2020, pp. 1155–62.
APA
Singh, P., Bauwelinck, N., & Lefever, E. (2020). LT3 at SemEval-2020 Task 8 : multi-modal multi-task learning for memotion analysis. In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020) (pp. 1155–1162). Barcelona, Spain: Association for Computational Linguistics (ACL).
Chicago author-date
Singh, Pranaydeep, Nina Bauwelinck, and Els Lefever. 2020. “LT3 at SemEval-2020 Task 8 : Multi-Modal Multi-Task Learning for Memotion Analysis.” In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 1155–62. Barcelona, Spain: Association for Computational Linguistics (ACL).
Chicago author-date (all authors)
Singh, Pranaydeep, Nina Bauwelinck, and Els Lefever. 2020. “LT3 at SemEval-2020 Task 8 : Multi-Modal Multi-Task Learning for Memotion Analysis.” In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 1155–1162. Barcelona, Spain: Association for Computational Linguistics (ACL).
Vancouver
1.
Singh P, Bauwelinck N, Lefever E. LT3 at SemEval-2020 Task 8 : multi-modal multi-task learning for memotion analysis. In: Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020). Barcelona, Spain: Association for Computational Linguistics (ACL); 2020. p. 1155–62.
IEEE
[1]
P. Singh, N. Bauwelinck, and E. Lefever, “LT3 at SemEval-2020 Task 8 : multi-modal multi-task learning for memotion analysis,” in Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), Barcelona, Spain (online), 2020, pp. 1155–1162.
@inproceedings{8684799,
  abstract     = {{Internet memes have become a very popular mode of expression on social media networks today.
Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging
research object for automatic analysis. In this paper, we describe our contribution to the SemEval2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which
incorporates “memebeddings”, viz. joint text and vision features, to learn and optimize for all
three Memotion subtasks simultaneously. The experimental results show that the proposed system
constantly outperforms the competition’s baseline, and the system setup with continual learning
(where tasks are trained sequentially) obtains the best classification F1-scores.}},
  author       = {{Singh, Pranaydeep and Bauwelinck, Nina and Lefever, Els}},
  booktitle    = {{Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020)}},
  isbn         = {{9781952148316}},
  keywords     = {{LT3}},
  language     = {{eng}},
  location     = {{Barcelona, Spain (online)}},
  pages        = {{1155--1162}},
  publisher    = {{Association for Computational Linguistics (ACL)}},
  title        = {{LT3 at SemEval-2020 Task 8 : multi-modal multi-task learning for memotion analysis}},
  url          = {{https://www.aclweb.org/anthology/2020.semeval-1.0.pdf#page.1155}},
  year         = {{2020}},
}