Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for the target task of detecting emotions in context.
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).
Emotion recognition in conversations is crucial for building empathetic machines.
#2 best model for Emotion Recognition in Conversation on IEMOCAP
Emotion recognition in conversations is crucial for the development of empathetic machines.
#3 best model for Emotion Recognition in Conversation on IEMOCAP
Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication.
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
Several lexica for sentiment analysis have been developed and made available in the NLP community.
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.