  Emotion Recognition in Conversations (ERC) has been gaining increasing
importance as conversational agents become more and more common. Recognizing
emotions is key for effective communication, being a crucial component in the
development of effective and empathetic conversational agents. Knowledge and
understanding of the conversational context are extremely valuable for
identifying the emotions of the interlocutor. We thus approach Emotion
Recognition in Conversations leveraging the conversational context, i.e.,
taking into attention previous conversational turns. The usual approach to
model the conversational context has been to produce context-independent
representations of each utterance and subsequently perform contextual modeling
of these. Here we propose context-dependent embedding representations of each
utterance by leveraging the contextual representational power of pre-trained
transformer language models. In our approach, we feed the conversational
context appended to the utterance to be classified as input to the RoBERTa
encoder, to which we append a simple classification module, thus discarding the
need to deal with context after obtaining the embeddings since these constitute
already an efficient representation of such context. We also investigate how
the number of introduced conversational turns influences our model performance.
The effectiveness of our approach is validated on the open-domain DailyDialog
dataset and on the task-oriented EmoWOZ dataset.
