@inproceedings{u-hegde-etal-2021-uvce,
title = "{UVCE}-{IIITT}@{D}ravidian{L}ang{T}ech-{EACL}2021: {T}amil Troll Meme Classification: You need to Pay more Attention",
author = "U Hegde, Siddhanth and
Hande, Adeep and
Priyadharshini, Ruba and
Thavareesan, Sajeetha and
Chakravarthi, Bharathi Raja",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Kumar M, Anand and
Krishnamurthy, Parameswari and
Sherly, Elizabeth",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.dravidianlangtech-1.24",
pages = "180--186",
abstract = "Tamil is a Dravidian language that is commonly used and spoken in the southern part of Asia. During the 21st century and in the era of social media, memes have been a fun moment during the day to day life of people. Here, we try to analyze the true meaning of Tamil memes by classifying them as troll or non-troll. We present an ingenious model consisting of transformer-transformer architecture that tries to attain state of the art by using attention as its main component. The dataset consists of troll and non-troll images with their captions as texts. The task is a binary classification task. The objective of the model was to pay more and more attention to the extracted features and to ignore the noise in both images and text.",
}
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<abstract>Tamil is a Dravidian language that is commonly used and spoken in the southern part of Asia. During the 21st century and in the era of social media, memes have been a fun moment during the day to day life of people. Here, we try to analyze the true meaning of Tamil memes by classifying them as troll or non-troll. We present an ingenious model consisting of transformer-transformer architecture that tries to attain state of the art by using attention as its main component. The dataset consists of troll and non-troll images with their captions as texts. The task is a binary classification task. The objective of the model was to pay more and more attention to the extracted features and to ignore the noise in both images and text.</abstract>
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%0 Conference Proceedings
%T UVCE-IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention
%A U Hegde, Siddhanth
%A Hande, Adeep
%A Priyadharshini, Ruba
%A Thavareesan, Sajeetha
%A Chakravarthi, Bharathi Raja
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Kumar M, Anand
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%S Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv
%F u-hegde-etal-2021-uvce
%X Tamil is a Dravidian language that is commonly used and spoken in the southern part of Asia. During the 21st century and in the era of social media, memes have been a fun moment during the day to day life of people. Here, we try to analyze the true meaning of Tamil memes by classifying them as troll or non-troll. We present an ingenious model consisting of transformer-transformer architecture that tries to attain state of the art by using attention as its main component. The dataset consists of troll and non-troll images with their captions as texts. The task is a binary classification task. The objective of the model was to pay more and more attention to the extracted features and to ignore the noise in both images and text.
%U https://aclanthology.org/2021.dravidianlangtech-1.24
%P 180-186
Markdown (Informal)
[UVCE-IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention](https://aclanthology.org/2021.dravidianlangtech-1.24) (U Hegde et al., DravidianLangTech 2021)
ACL