EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
This work addresses the problem of enriching conversational analysis for researchers and developers of natural dialogue systems, but it is incremental as it applies existing methods to new data.
The authors tackled the lack of dialogue act labels in emotion corpora by using an ensemble of recurrent neural models to annotate IEMOCAP and MELD with dialogue acts, discovering specific co-occurrence patterns such as Accept/Agree with Joy and Apology with Sadness.
The recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation 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 corpora 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 and without context. These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label. We annotated two accessible multi-modal emotion corpora: IEMOCAP and MELD. We analyzed 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 Acts (EDA) corpus publicly available to the research community for further study and analysis.