live chat customer service

In this paper, we present EZCAT, an easy-to-use interface to annotate conversations in a two-level configurable schema, leveraging message-level labels and conversation-level labels at once.

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. [...]

In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges [...]