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.
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 [...]
In this paper, we place ourselves in a classification scenario in which the target classes and data type are not accessible during training. We use a meta-learning approach to [...]
Dans cet article nous reproduisons un scénario d'apprentissage selon lequel les données cibles ne sont pas accessibles et seules des données connexes le sont. [...]
Annotation tool to annotate conversations with emotions, satisfaction and problem resolution status.