LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification
Waleed Ragheb, Jérôme Azé, Sandra Bringay, Maximilien Servajean · 2019
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) turnbased conversational modeling for classifying the emotions.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.