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. There is, however, a scarcity of research focusing on embracing positivity and reinforcing supportive and reassuring content in online forums. As a result, we concentrate our research on developing systems to detect hope speech in code-mixed Kannada. As a result, we present DC-LM, a dual-channel language model that sees hope speech by using the English translations of the code-mixed dataset for additional training. The approach is jointly modelled on both English and code-mixed Kannada to enable effective cross-lingual transfer between the languages. With a weighted F1-score of 0.756, the method outperforms other models. We aim to initiate research in Kannada while encouraging researchers to take a pragmatic approach to inspire positive and supportive online content.

We present our findings from the first shared task on Multi-task Learning in Dravidian Languages at the second Workshop on Speech and Language Technologies for Dravidian Languages. In this task, a sentence in any of three Dravidian Languages is required to be classified into two closely related tasks namely Sentiment Analyis (SA) and Offensive Language Identification (OLI). The task spans over three Dravidian Languages, namely, Kannada, Malayalam, and Tamil. 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. In total, 55 people signed up to participate in the task, and due to the intricate nature of the task, especially in its first iteration, 3 submissions have been received.

The social media is one of the significantdigital platforms that create a huge im-pact in peoples of all levels. The commentsposted on social media is powerful enoughto even change the political and businessscenarios in very few hours. They alsotend to attack a particular individual ora group of individuals. This shared taskaims at detecting the abusive comments in-volving, Homophobia, Misandry, Counter-speech, Misogyny, Xenophobia, Transpho-bic. The hope speech is also identified. Adataset collected from social media taggedwith the above said categories in Tamiland Tamil-English code-mixed languagesare given to the participants. The par-ticipants used different machine learningand deep learning algorithms. This paperpresents the overview of this task compris-ing the dataset details and results of theparticipants.

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.