Workshop on Multimodal Machine Learning in Low-resource Languages (2022)


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Proceedings of the First Workshop on Multimodal Machine Learning in Low-resource Languages

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Proceedings of the First Workshop on Multimodal Machine Learning in Low-resource Languages
Bharathi Raja Chakravarthi | Abirami Murugappan | Dhivya Chinnappa | Adeep Hane | Prasanna Kumar Kumeresan | Rahul Ponnusamy

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Multimodal Code-Mixed Tamil Troll Meme Classification using Feature Fusion
Ramesh Kannan | Ratnavel Rajalakshmi

Memes became an important way of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.

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Understanding the role of Emojis for emotion detection in Tamil
Ratnavel Rajalakshmi | Faerie Mattins R | Srivarshan Selvaraj | Antonette Shibani | Anand Kumar M | Bharathi Raja Chakravarthi

of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.