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

SEMEVAL 2019  ·  Rafa{\l} Po{\'s}wiata ·

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 combination 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 systems.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Emotion Recognition in Conversation EC ConSSED Micro-F1 0.7664 # 4


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