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Found 16 papers, 10 papers with code
DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil
With the rise of social media and internet, thereis a necessity to provide an inclusive space andprevent the abusive topics against any gender, race or community.
Hypers at ComMA@ICON: Modelling Aggressive, Gender Bias and Communal Bias Identification
Due to the exponential increasing reach of social media, it is essential to focus on its negative aspects as it can potentially divide society and incite people into violence.
Findings of the Shared Task on Multi-task Learning in Dravidian Languages
It is one of the first shared tasks that focuses on Multi-task Learning for closely related tasks, especially for a very low-resourced language family such as the Dravidian language family.
Findings of the Shared Task on Emotion Analysis in Tamil
This paper presents the dataset used in the shared task, task description, and the methodology used by the participants and the evaluation results of the submission.
IIITT at CASE 2021 Task 1: Leveraging Pretrained Language Models for Multilingual Protest Detection
In a world abounding in constant protests resulting from events like a global pandemic, climate change, religious or political conflicts, there has always been a need to detect events/protests before getting amplified by news media or social media.
The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada
In recent years, various methods have been developed to control the spread of negativity by removing profane, aggressive, and offensive comments from social media platforms.
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian Languages
This paper demonstrates our work for the shared task on Offensive Language Identification in Dravidian Languages-EACL 2021.
KanCMD: Kannada CodeMixed Dataset for Sentiment Analysis and Offensive Language Detection
We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification.
Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification
Due to the exponentially increasing reach of social media, it is essential to focus on its negative aspects as it can potentially divide society and incite people into violence.
Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text
We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English.
Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling
We fine-tune several recent pretrained language models on the newly constructed dataset.
Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021
This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English->Marathi and English->Irish language pairs LoResMT 2021 shared task.
Hope Speech detection in under-resourced Kannada language
Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms.
Ranked #1 on
Hope Speech Detection
on KanHope
Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification
Our work illustrates different textual analysis methods and contrasting multimodal methods ranging from simple merging to cross attention to utilising both worlds' - best visual and textual features.
UVCE-IIITT@DravidianLangTech-EACL2021: Tamil Troll Meme Classification: You need to Pay more Attention
We propose an ingenious model comprising of a transformer-transformer architecture that tries to attain state-of-the-art by using attention as its main component.
Ranked #2 on
Meme Classification
on Tamil Memes
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in Transformers
In a world filled with serious challenges like climate change, religious and political conflicts, global pandemics, terrorism, and racial discrimination, an internet full of hate speech, abusive and offensive content is the last thing we desire for.
