MoonGrad at SemEval-2019 Task 3: Ensemble BiRNNs for Contextual Emotion Detection in Dialogues

When reading “I don’t want to talk to you any more”, we might interpret this as either an an- gry or a sad emotion in the absence of context. Often, the utterances are shorter, and given a short utterance like “Me too!”, it is dif?cult to interpret the emotion without context. The lack of prosodic or visual information makes it a challenging problem to detect such emo- tions only with text. However, using contex- tual information in the dialogue is gaining im- portance to provide a context-aware recogni- tion of linguistic features such as emotion, di- alogue act, sentiment etc. The SemEval 2019 Task 3 EmoContext competition provides a dataset of three-turn dialogues labeled with the three emotion classes, i.e. Happy,Sad and An- gry, and in addition with Others as none of the aforementioned emotion classes. We develop an ensemble of the recurrent neural model with character- and word-level features as an in- put to solve this problem. The system per- forms quite well, achieving a microaveraged F1 score of 0.7212 for the three emotion classes.

@InProceedings{BW19, 
 	 author =  {Bothe, Chandrakant and Wermter, Stefan},  
 	 title = {MoonGrad at SemEval-2019 Task 3: Ensemble BiRNNs for Contextual Emotion Detection in Dialogues}, 
 	 booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {261--265},
 	 year = {2019},
 	 month = {Jun},
 	 publisher = {Association for Computational Linguistics},
 	 doi = {10.18653/v1/S19-2044}, 
 }