  Active research pertaining to the affective phenomenon of empathy and
distress is invaluable for improving human-machine interaction. Predicting
intensities of such complex emotions from textual data is difficult, as these
constructs are deeply rooted in the psychological theory. Consequently, for
better prediction, it becomes imperative to take into account ancillary factors
such as the psychological test scores, demographic features, underlying latent
primitive emotions, along with the text's undertone and its psychological
complexity. This paper proffers team PVG's solution to the WASSA 2021 Shared
Task on Predicting Empathy and Emotion in Reaction to News Stories. Leveraging
the textual data, demographic features, psychological test score, and the
intrinsic interdependencies of primitive emotions and empathy, we propose a
multi-input, multi-task framework for the task of empathy score prediction.
Here, the empathy score prediction is considered the primary task, while
emotion and empathy classification are considered secondary auxiliary tasks.
For the distress score prediction task, the system is further boosted by the
addition of lexical features. Our submission ranked 1$^{st}$ based on the
average correlation (0.545) as well as the distress correlation (0.574), and
2$^{nd}$ for the empathy Pearson correlation (0.517).
