Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.

Automatic Sarcasm Detection in conversations is a difficult and tricky task. Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on its context. This paper presents our proposed model, C-Net, which takes contextual information of a sentence in a sequential manner to classify it as sarcastic or non-sarcastic. Our model showcases competitive performance in the Sarcasm Detection shared task organised on CodaLab and achieved 75.0% F1-score on the Twitter dataset and 66.3% F1-score on Reddit dataset.