  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.
