S pan E mo: Casting Multi-label Emotion Classification as Span-prediction (2021)

Abstract: 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.

## introduction emotion is essential to human communication, thus emotion recognition (er) models have a host of applications from health and well-being @xcite @xcite to consumer analysis @xcite and user profiling @xcite , among others. interest in this area has given rise to new nlp approaches aimed at emotion classification, including single-label and multi-label emotion classification. most existing approaches for multi-label emotion classi-fication @xcite @xcite @xcite @xcite do not effectively capture emotion-specific associations, which can be useful for prediction, as well as learning of association between emotion labels and words in a sentence. in addition, standard approaches in emotion classification treat individual emotion independently. however, emotions are not independent; a specific emotive expression can be associated with multiple emotions. the existence of association/correlation among emotions has been well-studied in psychological theories of emotions, such as plutchik's wheels of emotion @xcite that introduces the notion of mixed and contrastive emotions. for example, "joy" is close to "love" and "optimism", instead of "anger" and "sadness". ## results as shown in based on the empirical results reported in ## related work there is a large body of nlp literature on emotion recognition @xcite . earlier studies focused on lexicon-based approaches, which make use of some words and their corresponding labels to identify emotions in text, e.g. nrc foot_3 @xcite and emosenticnet @xcite . other methods treat the emotion recognition task as a supervised learning task, in which a learner (e.g. linear classifier based methods) is trained on the features of labelled data to classify inputs into one label @xcite @xcite @xcite . more recently, several neural network models have been developed for this task, obtaining competitive results on different emotion data sets. some of these models generally focus on a singlelabel emotion classification, in which only a single label is assigned to each input @xcite @xcite @xcite @xcite . other models have also been proposed for multilabel emotion classification, in which one or more labels are assigned to each input (see detailed description in section 3.3). our work is motivated by research focused on learning features corresponding to each emotion as well as incorporating the relations between emotions into a loss function @xcite . our work differs from these two works in the following ways: i) our method learns features related to each corresponding emotion without relying on any external resources (e.g. lexicons). ii) we further integrated the relations between emotions into the loss function by taking advantage of the label co-occurrences in a multi-label emotion data set. in this respect, our approach does not rely on any theory of emotion. iii) we empirically evaluated our method for three languages, demonstrating its effectiveness as being language agnostic. in contrast to previous research, we focus on both learning emotion-specific associations and integrating the relations between emotions into the loss function. ## conclusion we have proposed a novel framework "spanemo" aimed at casting multi-label emotion classification as a span-prediction problem. we demonstrated that our proposed method outperforms prior approaches reported in the literature on three languages (i.e., english, arabic and spanish). our empirical evaluation and analyses also demonstrated the utility and advantages of our method for multilabel emotion classification, specifically the addi-tion of emotion classes to the input sentence, which helped the model learn emotion-specific associations and increase its performance. finally, training our method with lca loss jointly led to better results, showing the benefits of integrating the relations between emotions into the loss function. the standard approach in a multi-label emotion classification problem often focuses on modelling individual emotion independently. in this respect, most existing methods do not take into account label dependencies while learning emotion-specific associations. however, we demonstrated the effectiveness of including label information to the input sentence when training spanemo, helping it achieve better performance and capture emotion correlations. we hope that this study will inspire the community to investigate further the vital role of learning label dependencies and associations corresponding to each emotion.