  Emotion is fundamental to humanity. The ability to perceive, understand and
respond to social interactions in a human-like manner is one of the most
desired capabilities in artificial agents, particularly in social-media bots.
Over the past few years, computational understanding and detection of emotional
aspects in language have been vital in advancing human-computer interaction.
The WASSA Shared Task 2021 released a dataset of news-stories across two
tracks, Track-1 for Empathy and Distress Prediction and Track-2 for
Multi-Dimension Emotion prediction at the essay-level. We describe our system
entry for the WASSA 2021 Shared Task (for both Track-1 and Track-2), where we
leveraged the information from Pre-trained language models for Track-specific
Tasks. Our proposed models achieved an Average Pearson Score of 0.417 and a
Macro-F1 Score of 0.502 in Track 1 and Track 2, respectively. In the Shared
Task leaderboard, we secured 4th rank in Track 1 and 2nd rank in Track 2.
