Symanto Research at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations

By: Angelo Basile, Marc Franco-Salvador, Neha Pawar, Sanja Stajner, Mara Chinea Rios, and Yassine Benajiba

Year: 2019

Abstract 

In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further improve the results. We show that our neural ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY) and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.
 

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