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

SpanEmo: Casting Multi-label Emotion Classification as Span-prediction