  This paper addresses the problem of modeling textual conversations and
detecting emotions. Our proposed model makes use of 1) deep transfer learning
rather than the classical shallow methods of word embedding; 2) self-attention
mechanisms to focus on the most important parts of the texts and 3) turn-based
conversational modeling for classifying the emotions. The approach does not
rely on any hand-crafted features or lexicons. Our model was evaluated on the
data provided by the SemEval-2019 shared task on contextual emotion detection
in text. The model shows very competitive results.
