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An Adaptive Layer to Leverage Both Domain and Task Specific Information from Scarce Data

In this paper, we present our method, TAFT, to enhance model adaptation both on domain and task especially for small datasets. To demonstrate it we consider the use-case of customer service chats.

EZCAT: an Easy Conversation Annotation Tool

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.

Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks

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 [...]

Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional Categories

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 [...]

Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement

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. [...]