  The need for emotional inference from text continues to diversify as more and
more disciplines integrate emotions into their theories and applications. These
needs include inferring different emotion types, handling multiple languages,
and different annotation formats. A shared model between different
configurations would enable the sharing of knowledge and a decrease in training
costs, and would simplify the process of deploying emotion recognition models
in novel environments. In this work, we study how we can build a single model
that can transition between these different configurations by leveraging
multilingual models and Demux, a transformer-based model whose input includes
the emotions of interest, enabling us to dynamically change the emotions
predicted by the model. Demux also produces emotion embeddings, and performing
operations on them allows us to transition to clusters of emotions by pooling
the embeddings of each cluster. We show that Demux can simultaneously transfer
knowledge in a zero-shot manner to a new language, to a novel annotation format
and to unseen emotions. Code is available at
https://github.com/gchochla/Demux-MEmo .
