EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators

The recognition of emotion and dialogue acts enrich conversational analysis and help to build natural dialogue systems. Emotion makes us understand feelings and dialogue acts reflect the intentions and performative functions in the utterances. However, most of the textual and multi-modal conversational emotion datasets contain only emotion labels but not dialogue acts. To address this problem, we propose to use a pool of various recurrent neural models trained on a dialogue act corpus, with or without context. These neural models annotate the emotion corpus with dialogue act labels and an ensemble annotator extracts the final dialogue act label. We annotated two popular multi-modal emotion datasets: IEMOCAP and MELD. We analysed the co-occurrence of emotion and dialogue act labels and discovered specific relations. For example, Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy. We make the Emotional Dialogue Act (EDA) corpus publicly available to the research community for further study and analysis.

@Article{BWMW20, 
 	 author =  {Bothe, Chandrakant and Weber, Cornelius and Magg, Sven and Wermter, Stefan},  
 	 title = {EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators}, 
 	 journal = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC 2020)},
 	 number = {},
 	 volume = {},
 	 pages = {620--627},
 	 year = {2020},
 	 month = {May},
 	 publisher = {European Language Resources Association (ELRA)},
 	 doi = {https://www.aclweb.org/anthology/2020.lrec-1.78}, 
 }