@inproceedings{alhuzali-ananiadou-2021-spanemo,
title = "{S}pan{E}mo: Casting Multi-label Emotion Classification as Span-prediction",
author = "Alhuzali, Hassan and
Ananiadou, Sophia",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.135",
doi = "10.18653/v1/2021.eacl-main.135",
pages = "1573--1584",
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.",
}
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%0 Conference Proceedings
%T SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
%A Alhuzali, Hassan
%A Ananiadou, Sophia
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F alhuzali-ananiadou-2021-spanemo
%X 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.
%R 10.18653/v1/2021.eacl-main.135
%U https://aclanthology.org/2021.eacl-main.135
%U https://doi.org/10.18653/v1/2021.eacl-main.135
%P 1573-1584
Markdown (Informal)
[SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://aclanthology.org/2021.eacl-main.135) (Alhuzali & Ananiadou, EACL 2021)
ACL