{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spanemo-casting-multi-label-emotion","title":"SpanEmo: Casting Multi-label Emotion Classification as Span-prediction","arxiv_id":"2101.10038","date":"2021-01-25","proceeding":"EACL 2021 2","authors":["Hassan Alhuzali","Sophia Ananiadou"],"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.","url_abs":"https://arxiv.org/abs/2101.10038v1","url_pdf":"https://arxiv.org/pdf/2101.10038v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spanemo-casting-multi-label-emotion","repo_url":"https://github.com/hasanhuz/SpanEmo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"author-profiling","task_name":"Author Profiling"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-classification-on-semeval-2018-task","task":"Emotion Classification","dataset":"SemEval 2018 Task 1E-c","model":"SpanEmo","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"0.601","Macro-F1":"0.578","Micro-F1":"0.713"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2101.10038","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}