ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector

Rafał Poświata

2019 W: Proceedings of the 13th International Workshop on Semantic Evaluation / Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutowa, Aurelie Herbelot, Zhu Xiaodan; Minneapolis: Association for Computational Linguistics, s. 175-179

13th International Workshop on Semantic Evaluation. Minneapolis, 2019-06-06 - 2019-06-07

This paper describes our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text. The goal was to for a given textual dialogue, i.e. a user utterance along with two turns of context, identify the emotion of user utterance as one of the emotion classes: Happy, Sad, Angry or Others. Our system: ConSSED is a configurable ombination of semantic and sentiment
neural models. The official task submission
achieved a micro-average F1 score of 75.31
which placed us 16th out of 165 participating