  Empathy and emotion prediction are key components in the development of
effective and empathetic agents, amongst several other applications. The WASSA
shared task on empathy and emotion prediction in interactions presents an
opportunity to benchmark approaches to these tasks. Appropriately selecting and
representing the historical context is crucial in the modelling of empathy and
emotion in conversations. In our submissions, we model empathy, emotion
polarity and emotion intensity of each utterance in a conversation by feeding
the utterance to be classified together with its conversational context, i.e.,
a certain number of previous conversational turns, as input to an encoder
Pre-trained Language Model, to which we append a regression head for
prediction. We also model perceived counterparty empathy of each interlocutor
by feeding all utterances from the conversation and a token identifying the
interlocutor for which we are predicting the empathy. Our system officially
ranked $1^{st}$ at the CONV-turn track and $2^{nd}$ at the CONV-dialog track.
