  Sarcasm is an intricate form of speech, where meaning is conveyed implicitly.
Being a convoluted form of expression, detecting sarcasm is an assiduous
problem. The difficulty in recognition of sarcasm has many pitfalls, including
misunderstandings in everyday communications, which leads us to an increasing
focus on automated sarcasm detection. In the second edition of the Figurative
Language Processing (FigLang 2020) workshop, the shared task of sarcasm
detection released two datasets, containing responses along with their context
sampled from Twitter and Reddit.
  In this work, we use RoBERTa_large to detect sarcasm in both the datasets. We
further assert the importance of context in improving the performance of
contextual word embedding based models by using three different types of inputs
- Response-only, Context-Response, and Context-Response (Separated). We show
that our proposed architecture performs competitively for both the datasets. We
also show that the addition of a separation token between context and target
response results in an improvement of 5.13% in the F1-score in the Reddit
dataset.
