SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)
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| Format: | Preprint |
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2024
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| _version_ | 1866911786333110272 |
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| author | Kumar, Shivani Akhtar, Md Shad Cambria, Erik Chakraborty, Tanmoy |
| author_facet | Kumar, Shivani Akhtar, Md Shad Cambria, Erik Chakraborty, Tanmoy |
| contents | We present SemEval-2024 Task 10, a shared task centred on identifying emotions and finding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for code-mixed dialogues, emotion flip reasoning for code-mixed dialogues, and emotion flip reasoning for English dialogues. Participating systems were tasked to automatically execute one or more of these subtasks. The datasets for these tasks comprise manually annotated conversations focusing on emotions and triggers for emotion shifts (The task data is available at https://github.com/LCS2-IIITD/EDiReF-SemEval2024.git). A total of 84 participants engaged in this task, with the most adept systems attaining F1-scores of 0.70, 0.79, and 0.76 for the respective subtasks. This paper summarises the results and findings from 24 teams alongside their system descriptions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_18944 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF) Kumar, Shivani Akhtar, Md Shad Cambria, Erik Chakraborty, Tanmoy Computation and Language Artificial Intelligence We present SemEval-2024 Task 10, a shared task centred on identifying emotions and finding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for code-mixed dialogues, emotion flip reasoning for code-mixed dialogues, and emotion flip reasoning for English dialogues. Participating systems were tasked to automatically execute one or more of these subtasks. The datasets for these tasks comprise manually annotated conversations focusing on emotions and triggers for emotion shifts (The task data is available at https://github.com/LCS2-IIITD/EDiReF-SemEval2024.git). A total of 84 participants engaged in this task, with the most adept systems attaining F1-scores of 0.70, 0.79, and 0.76 for the respective subtasks. This paper summarises the results and findings from 24 teams alongside their system descriptions. |
| title | SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF) |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.18944 |