Summary of Petkaz at Semeval-2024 Task 3: Advancing Emotion Classification with An Llm For Emotion-cause Pair Extraction in Conversations, by Roman Kazakov et al.
PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
by Roman Kazakov, Kseniia Petukhova, Ekaterina Kochmar
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a solution to SemEval-2023 Task~3’s “Multimodal Emotion Cause Analysis in Conversations” challenge, focusing on extracting emotion-cause pairs from dialogues. The approach combines fine-tuned GPT-3.5 for emotion classification with a BiLSTM-based neural network to detect causes. The paper demonstrates the effectiveness of this approach through one of the highest weighted-average proportional F1 scores recorded at 0.264. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the reasons why people feel certain emotions in conversations. It’s part of a bigger competition that wants to improve how computers understand human emotions and what causes them. The authors came up with an idea that combines two different AI techniques: one that can recognize emotions, and another that can find patterns in text. They tested it and got really good results, which shows that their approach is effective. |
Keywords
» Artificial intelligence » Classification » Gpt » Neural network
