WASSA 2021 shared task : predicting empathy and emotion in reaction to news stories

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Tafreshi, S., De Clercq, O., Barriere, V., Sedoc, J., Buechel, S., & Balahur, A.
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021)
Association for Computational Linguistics
Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), held in conjunction with EACL 2021 (Online)
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This paper presents the results that were obtained from the WASSA 2021 shared task on predicting empathy and emotions. The participants were given access to a dataset comprising empathic reactions to news stories where harm is done to a person, group, or other. These reactions consist of essays, Batson empathic concern, and personal distress scores, and the dataset was further extended with news articles, person-level demographic information (age, gender, ethnicity, income, education level), and personality information. Additionally, emotion labels, namely Ekman's six basic emotions, were added to the essays at both the document and sentence level. Participation was encouraged in two tracks: predicting empathy and predicting emotion categories. In total five teams participated in the shared task. We summarize the methods and resources used by the participating teams.