A computational theory of grounding in natural language conversation
A computational theory of grounding in natural language conversation
Standoff coordination for multi-tool annotation in a dialogue corpus
LAW '07 Proceedings of the Linguistic Annotation Workshop
On the contextual analysis of agreement scores
Multimodal corpora
An affect-enriched dialogue act classification model for task-oriented dialogue
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Combining verbal and nonverbal features to overcome the 'information gap' in task-oriented dialogue
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
| Hi-index | 0.00 |
In this paper we describe the Rovereto Emotive Corpus (REC) which we collected to investigate the relationship between emotion and cooperation in dialogue tasks. It is an area where still many unsolved questions are present. One of the main open issues is the annotation of the so-called "blended" emotions and their recognition. Usually, there is a low agreement among raters in annotating emotions and, surprisingly, emotion recognition is higher in a condition of modality deprivation (i. e. only acoustic or only visual modality vs. bimodal display of emotion). Because of these previous results, we collected a corpus in which "emotive" tokens are pointed out during the recordings by psychophysiological indexes (ElectroCardioGram, and Galvanic Skin Conductance). From the output values of these indexes a general recognition of each emotion arousal is allowed. After this selection we will annotate emotive interactions with our multimodal annotation scheme, performing a kappa statistic on annotation results to validate our coding scheme. In the near future, a logistic regression on annotated data will be performed to find out correlations between cooperation and negative emotions. A final step will be an fMRI experiment on emotion recognition of blended emotions from face displays.