@inproceedings{ghahramani-kure-etal-2024-aima,
title = "{AIMA} at {S}em{E}val-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis",
author = "Ghahramani Kure, Alireza and
Dehghani, Mahshid and
Abootorabi, Mohammad Mahdi and
Ghazizadeh, Nona and
Dalili, Seyed Arshan and
Asgari, Ehsaneddin",
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-1.243",
doi = "10.18653/v1/2024.semeval-1.243",
pages = "1698--1703",
abstract = "The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. Conversely, Subtask 2 extends the analysis to encompass multimodal cues, including language, audio, and vision, acknowledging instances where causes may not be exclusively represented in the textual data. Our proposed model for emotion-cause analysis is meticulously structured into three core segments: (i) embedding extraction, (ii) cause-pair extraction {\&} emotion classification, and (iii) cause extraction using QA after finding pairs. Leveraging state-of-the-art techniques and fine-tuning on task-specific datasets, our model effectively unravels the intricate web of conversational dynamics and extracts subtle cues signifying causality in emotional expressions. Our team, AIMA, demonstrated strong performance in the SemEval-2024 Task 3 competition. We ranked as the 10th in subtask 1 and the 6th in subtask 2 out of 23 teams.",
}
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<abstract>The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. Conversely, Subtask 2 extends the analysis to encompass multimodal cues, including language, audio, and vision, acknowledging instances where causes may not be exclusively represented in the textual data. Our proposed model for emotion-cause analysis is meticulously structured into three core segments: (i) embedding extraction, (ii) cause-pair extraction & emotion classification, and (iii) cause extraction using QA after finding pairs. Leveraging state-of-the-art techniques and fine-tuning on task-specific datasets, our model effectively unravels the intricate web of conversational dynamics and extracts subtle cues signifying causality in emotional expressions. Our team, AIMA, demonstrated strong performance in the SemEval-2024 Task 3 competition. We ranked as the 10th in subtask 1 and the 6th in subtask 2 out of 23 teams.</abstract>
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%0 Conference Proceedings
%T AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis
%A Ghahramani Kure, Alireza
%A Dehghani, Mahshid
%A Abootorabi, Mohammad Mahdi
%A Ghazizadeh, Nona
%A Dalili, Seyed Arshan
%A Asgari, Ehsaneddin
%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 ghahramani-kure-etal-2024-aima
%X The SemEval-2024 Task 3 presents two subtasks focusing on emotion-cause pair extraction within conversational contexts. Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. Conversely, Subtask 2 extends the analysis to encompass multimodal cues, including language, audio, and vision, acknowledging instances where causes may not be exclusively represented in the textual data. Our proposed model for emotion-cause analysis is meticulously structured into three core segments: (i) embedding extraction, (ii) cause-pair extraction & emotion classification, and (iii) cause extraction using QA after finding pairs. Leveraging state-of-the-art techniques and fine-tuning on task-specific datasets, our model effectively unravels the intricate web of conversational dynamics and extracts subtle cues signifying causality in emotional expressions. Our team, AIMA, demonstrated strong performance in the SemEval-2024 Task 3 competition. We ranked as the 10th in subtask 1 and the 6th in subtask 2 out of 23 teams.
%R 10.18653/v1/2024.semeval-1.243
%U https://aclanthology.org/2024.semeval-1.243
%U https://doi.org/10.18653/v1/2024.semeval-1.243
%P 1698-1703
Markdown (Informal)
[AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis](https://aclanthology.org/2024.semeval-1.243) (Ghahramani Kure et al., SemEval 2024)
ACL
- Alireza Ghahramani Kure, Mahshid Dehghani, Mohammad Mahdi Abootorabi, Nona Ghazizadeh, Seyed Arshan Dalili, and Ehsaneddin Asgari. 2024. AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1698–1703, Mexico City, Mexico. Association for Computational Linguistics.