The social media is one of the significantdigital platforms that create a huge im-pact in peoples of all levels.
no code implementations • • Bharathi Raja Chakravarthi, Gaman Mihaela, Radu Tudor Ionescu, Heidi Jauhiainen, Tommi Jauhiainen, Krister Lindén, Nikola Ljubešić, Niko Partanen, Ruba Priyadharshini, Christoph Purschke, Eswari Rajagopal, Yves Scherrer, Marcos Zampieri
This paper describes the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2021.
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.
This paper presents an outline of the shared task on translation of under-resourced Dravidian languages at DravidianLangTech-2022 workshop to be held jointly with ACL 2022.
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.
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 • • Bharathi Raja Chakravarthi, Vigneshwaran Muralidaran, Ruba Priyadharshini, Subalalitha Cn, John McCrae, Miguel Ángel García, Salud María Jiménez-Zafra, Rafael Valencia-García, Prasanna Kumaresan, Rahul Ponnusamy, Daniel García-Baena, José García-Díaz
Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences.
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.
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.
This paper describes the datasets used, the methodology used for the evaluation of participants, and the experiments’ overall results.
no code implementations • • Bharathi Raja Chakravarthi, Ruba Priyadharshini, Navya Jose, Anand Kumar M, Thomas Mandl, Prasanna Kumar Kumaresan, Rahul Ponnusamy, Hariharan R L, John P. McCrae, Elizabeth Sherly
Detecting offensive language in social media in local languages is critical for moderating user-generated content.
This paper demonstrates our work for the shared task on Offensive Language Identification in Dravidian Languages-EACL 2021.
We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification.
This shared taskfocused on three sub-tasks for Tamil, English, and Tamil-English (code-mixed) languages.
Thirumurai, also known as Panniru Thirumurai, is a collection of Tamil Shaivite poems dating back to the Hindu revival period between the 6th and the 10th century.
no code implementations • 9 Feb 2022 • Charangan Vasantharajan, Sean Benhur, Prasanna Kumar Kumarasen, Rahul Ponnusamy, Sathiyaraj Thangasamy, Ruba Priyadharshini, Thenmozhi Durairaj, Kanchana Sivanraju, Anbukkarasi Sampath, Bharathi Raja Chakravarthi, John Phillip McCrae
Our MURIL-base model has achieved a 0. 60 macro average F1-score across our 3-class group dataset.
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.
no code implementations • 5 Nov 2021 • Bharathi Raja Chakravarthi, Dhivya Chinnappa, Ruba Priyadharshini, Anand Kumar Madasamy, Sangeetha Sivanesan, Subalalitha Chinnaudayar Navaneethakrishnan, Sajeetha Thavareesan, Dhanalakshmi Vadivel, Rahul Ponnusamy, Prasanna Kumar Kumaresan
With the fast growth of mobile computing and Web technologies, offensive language has become more prevalent on social networking platforms.
no code implementations • 1 Sep 2021 • Bharathi Raja Chakravarthi, Ruba Priyadharshini, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Kayalvizhi Sampath, Durairaj Thenmozhi, Sathiyaraj Thangasamy, Rajendran Nallathambi, John Phillip McCrae
We provide a new hierarchical taxonomy for online homophobia and transphobia, as well as an expert-labelled dataset that will allow homophobic/transphobic content to be automatically identified.
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.
This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments.
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.
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
This paper describes the IIITK’s team submissions to the hope speech detection for equality, diversity and inclusion in Dravidian languages shared task organized by LT-EDI 2021 workshop@EACL 2021.
This paper describes the IIITK team’s submissions to the offensive language identification, and troll memes classification shared tasks for Dravidian languages at DravidianLangTech 2021 workshop@EACL 2021.
One such application is to analyse the popular sentiments of videos on social media based on viewer comments.