no code implementations • • Bharathi Raja Chakravarthi, Ruba Priyadharshini, Subalalitha Cn, Sangeetha S, Malliga Subramanian, Kogilavani Shanmugavadivel, Parameswari Krishnamurthy, Adeep Hande, Siddhanth U Hegde, Roshan Nayak, Swetha Valli
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.
no code implementations • • Anbukkarasi Sampath, Thenmozhi Durairaj, Bharathi Raja Chakravarthi, Ruba Priyadharshini, Subalalitha Cn, Kogilavani Shanmugavadivel, Sajeetha Thavareesan, Sathiyaraj Thangasamy, Parameswari Krishnamurthy, Adeep Hande, Sean Benhur, Kishore Ponnusamy, Santhiya Pandiyan
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.
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.
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.
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.
We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification.
This paper demonstrates our work for the shared task on Offensive Language Identification in Dravidian Languages-EACL 2021.
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.
no code implementations • 18 Nov 2021 • Bharathi Raja Chakravarthi, Ruba Priyadharshini, Sajeetha Thavareesan, Dhivya Chinnappa, Durairaj Thenmozhi, Elizabeth Sherly, John P. McCrae, Adeep Hande, Rahul Ponnusamy, Shubhanker Banerjee, Charangan Vasantharajan
We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English.
1 code implementation • 27 Aug 2021 • Adeep Hande, Karthik Puranik, Konthala Yasaswini, Ruba Priyadharshini, Sajeetha Thavareesan, Anbukkarasi Sampath, Kogilavani Shanmugavadivel, Durairaj Thenmozhi, Bharathi Raja Chakravarthi
We fine-tune several recent pretrained language models on the newly constructed dataset.
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.
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
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.
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.
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.