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.
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. [...]
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EMOtional EMOji RECOmmendation system. Web interface to use and evaluate the recommendation system in a textual chat.
Emoji Embeddings and Clusters of Face Emojis. This is the source code from a paper we presented to CICLing 2018, please cite this paper if you find this work useful.
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