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📢If you're in the emotion and emotion causes extraction domain and wish to bring something new in Generative AI, then the repository 
📢If you're in the emotion and emotion causes extraction domain and wish to bring something new in Generative AI, then the repository 
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⭐ 
⭐ 
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https://github.com/declare-lab/conv-emotion
https://github.com/declare-lab/conv-emotion
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Is worth to check out as a survey of the conventional method approaches.
Is worth to check out as a survey of the conventional method approaches.
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💎 It hosts implementation of the most conventional methods that are relevant even today in the era of Generative AI. For example, the variations of LSTM-based encoders are still represent a solid framework for it and according to the SemEval2024 Task 3 where it is possible to see several papers exploit such a conventional concept:
💎 It hosts implementation of the most conventional methods that are relevant even today in the era of Generative AI. For example, the variations of LSTM-based encoders are still represent a solid framework for it and according to the SemEval2024 Task 3 where it is possible to see several papers exploit such a conventional concept:
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https://aclanthology.org/volumes/2024.semeval-1/
https://aclanthology.org/volumes/2024.semeval-1/
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An example of the Top 3 submission:
An example of the Top 3 submission:
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https://aclanthology.org/2024.semeval-1.164/
https://aclanthology.org/2024.semeval-1.164/
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🤔 Similar techniques empowered by transformers such as XLSTM might be a promising step towards even more robust solutions:
🤔 Similar techniques empowered by transformers such as XLSTM might be a promising step towards even more robust solutions:
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link
https://github.com/NX-AI/xlstm
https://github.com/NX-AI/xlstm
📢If you're in the emotion and emotion causes extraction domain and wish to bring something new in Generative AI, then the repository 

⭐ https://github.com/declare-lab/conv-emotion

Is worth to check out as a survey of the conventional method approaches.

💎 It hosts implementation of the most conventional methods that are relevant even today in the era of Generative AI. For example, the variations of LSTM-based encoders are still represent a solid framework for it and according to the SemEval2024 Task 3 where it is possible to see several papers exploit such a conventional concept:
https://aclanthology.org/volumes/2024.semeval-1/
An example of the Top 3 submission:
https://aclanthology.org/2024.semeval-1.164/

🤔 Similar techniques empowered by transformers such as XLSTM might be a promising step towards even more robust solutions:
https://github.com/NX-AI/xlstm
👀
John6666