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Several recent studies on dyadic human-human interactions have been done onconversations without specific business objectives. However, many companiesmight benefit from studies dedicated to more precise environments such as aftersales services or customer satisfaction surveys. In this work, we placeourselves in the scope of a live chat customer service in which we want todetect emotions and their evolution in the conversation flow. This contextleads to multiple challenges that range from exploiting restricted, small andmostly unlabeled datasets to finding and adapting methods for such context.Wetackle these challenges by using Few-Shot Learning while making the hypothesisit can serve conversational emotion classification for different languages andsparse labels. We contribute by proposing a variation of Prototypical Networksfor sequence labeling in conversation that we name ProtoSeq. We test thismethod on two datasets with different languages: daily conversations in Englishand customer service chat conversations in French. When applied to emotionclassification in conversations, our method proved to be competitive even whencompared to other ones.

Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks