  Emotion recognition (ER) is an important task in Natural Language Processing
(NLP), due to its high impact in real-world applications from health and
well-being to author profiling, consumer analysis and security. Current
approaches to ER, mainly classify emotions independently without considering
that emotions can co-exist. Such approaches overlook potential ambiguities, in
which multiple emotions overlap. We propose a new model "SpanEmo" casting
multi-label emotion classification as span-prediction, which can aid ER models
to learn associations between labels and words in a sentence. Furthermore, we
introduce a loss function focused on modelling multiple co-existing emotions in
the input sentence. Experiments performed on the SemEval2018 multi-label
emotion data over three language sets (i.e., English, Arabic and Spanish)
demonstrate our method's effectiveness. Finally, we present different analyses
that illustrate the benefits of our method in terms of improving the model
performance and learning meaningful associations between emotion classes and
words in the sentence.
