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SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)

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arxiv 2402.18944 v1 pith:H24QPEEB submitted 2024-02-29 cs.CL cs.AI

Shivani Kumar , Md Shad Akhtar , Erik Cambria , Tanmoy Chakraborty This is my paper · ORCID
classification cs.CLcs.AI
keywords taskemotiondialoguescode-mixedflipreasoningsubtasksconversation
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations

    cs.CL 2025-01 conditional novelty 3.0 of 10

    An ensemble of four context-aware models with a Hinglish-to-English translation pipeline achieves weighted F1 0.4080 on SemEval 2024 Task 10 subtask 1, marginally above its strongest single model.

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