AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913656325799936 |
|---|---|
| author | Kure, Alireza Ghahramani Dehghani, Mahshid Abootorabi, Mohammad Mahdi Ghazizadeh, Nona Dalili, Seyed Arshan Asgari, Ehsaneddin |
| author_facet | Kure, Alireza Ghahramani Dehghani, Mahshid Abootorabi, Mohammad Mahdi Ghazizadeh, Nona Dalili, Seyed Arshan Asgari, Ehsaneddin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis Kure, Alireza Ghahramani Dehghani, Mahshid Abootorabi, Mohammad Mahdi Ghazizadeh, Nona Dalili, Seyed Arshan Asgari, Ehsaneddin Computation and Language Artificial Intelligence Machine Learning 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. |
| title | AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.11170 |