@inproceedings{wan-etal-2024-ucsc,
title = "{UCSC} {NLP} at {S}em{E}val-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation ({ED}i{R}e{F})",
author = "Wan, Neng and
Au, Steven and
Ubale, Esha and
Krogh, Decker",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.214",
doi = "10.18653/v1/2024.semeval-1.214",
pages = "1492--1497",
abstract = "We describe SemEval-2024 Task 10: EDiReF consisting of three sub-tasks involving emotion in conversation across Hinglish code-mixed and English datasets. Subtasks include classification of speaker emotion in multiparty conversations (Emotion Recognition in Conversation) and reasoning around shifts in speaker emotion state (Emotion Flip Reasoning). We deployed a BERT model for emotion recognition and two GRU-based models for emotion flip. Our model achieved F1 scores of 0.45, 0.79, and 0.68 for subtasks 1, 2, and 3, respectively.",
}
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<abstract>We describe SemEval-2024 Task 10: EDiReF consisting of three sub-tasks involving emotion in conversation across Hinglish code-mixed and English datasets. Subtasks include classification of speaker emotion in multiparty conversations (Emotion Recognition in Conversation) and reasoning around shifts in speaker emotion state (Emotion Flip Reasoning). We deployed a BERT model for emotion recognition and two GRU-based models for emotion flip. Our model achieved F1 scores of 0.45, 0.79, and 0.68 for subtasks 1, 2, and 3, respectively.</abstract>
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%0 Conference Proceedings
%T UCSC NLP at SemEval-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)
%A Wan, Neng
%A Au, Steven
%A Ubale, Esha
%A Krogh, Decker
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F wan-etal-2024-ucsc
%X We describe SemEval-2024 Task 10: EDiReF consisting of three sub-tasks involving emotion in conversation across Hinglish code-mixed and English datasets. Subtasks include classification of speaker emotion in multiparty conversations (Emotion Recognition in Conversation) and reasoning around shifts in speaker emotion state (Emotion Flip Reasoning). We deployed a BERT model for emotion recognition and two GRU-based models for emotion flip. Our model achieved F1 scores of 0.45, 0.79, and 0.68 for subtasks 1, 2, and 3, respectively.
%R 10.18653/v1/2024.semeval-1.214
%U https://aclanthology.org/2024.semeval-1.214
%U https://doi.org/10.18653/v1/2024.semeval-1.214
%P 1492-1497
Markdown (Informal)
[UCSC NLP at SemEval-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)](https://aclanthology.org/2024.semeval-1.214) (Wan et al., SemEval 2024)
ACL