@inproceedings{maladry-etal-2024-findings,
title = "Findings of the {WASSA} 2024 {EXALT} shared task on Explainability for Cross-Lingual Emotion in Tweets",
author = "Maladry, Aaron and
Singh, Pranaydeep and
Lefever, Els",
editor = "De Clercq, Orph{\'e}e and
Barriere, Valentin and
Barnes, Jeremy and
Klinger, Roman and
Sedoc, Jo{\~a}o and
Tafreshi, Shabnam",
booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wassa-1.43",
pages = "454--463",
abstract = "This paper presents a detailed description and results of the first shared task on explainability for cross-lingual emotion in tweets. Given a tweet in one of the five target languages (Dutch, Russian, Spanish, English, and French), systems should predict the correct emotion label (Task 1), as well as the words triggering the predicted emotion label (Task 2). The tweets were collected based on a list of stop words to prevent topical or emotional bias and were subsequently manually annotated. For both tasks, only a training corpus for English was provided, obliging participating systems to design cross-lingual approaches. Our shared task received submissions from 14 teams for the emotion detection task and from 6 teams for the trigger word detection task. The highest macro F1-scores obtained for both tasks are respectively 0.629 and 0.616, demonstrating that cross-lingual emotion detection is still a challenging task.",
}
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%0 Conference Proceedings
%T Findings of the WASSA 2024 EXALT shared task on Explainability for Cross-Lingual Emotion in Tweets
%A Maladry, Aaron
%A Singh, Pranaydeep
%A Lefever, Els
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F maladry-etal-2024-findings
%X This paper presents a detailed description and results of the first shared task on explainability for cross-lingual emotion in tweets. Given a tweet in one of the five target languages (Dutch, Russian, Spanish, English, and French), systems should predict the correct emotion label (Task 1), as well as the words triggering the predicted emotion label (Task 2). The tweets were collected based on a list of stop words to prevent topical or emotional bias and were subsequently manually annotated. For both tasks, only a training corpus for English was provided, obliging participating systems to design cross-lingual approaches. Our shared task received submissions from 14 teams for the emotion detection task and from 6 teams for the trigger word detection task. The highest macro F1-scores obtained for both tasks are respectively 0.629 and 0.616, demonstrating that cross-lingual emotion detection is still a challenging task.
%U https://aclanthology.org/2024.wassa-1.43
%P 454-463
Markdown (Informal)
[Findings of the WASSA 2024 EXALT shared task on Explainability for Cross-Lingual Emotion in Tweets](https://aclanthology.org/2024.wassa-1.43) (Maladry et al., WASSA-WS 2024)
ACL