Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks
Abstract
Detecting emotions in a conversational context benefits several industrial cases such as customer service, user appraisal from speech recognition, and so on. However, in most cases, research data differ from real data due to them being private, confidential, or difficult to label. In this work we present ProtoSeq, an adaptation of the Prototypical Networks to enable dealing with sequences in a few-shot learning way, reducing the need for labeling confidential data.
Type
Publication
In the Elsevier Software Impact Journal
Gaël Guibon
Associate Professor
My research goes from emojis and emotion prediction and recommendation to meta learning, few-shot learning and French lexical evolution studies.