Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
Authors: Wei Ai, Fuchen Zhang, Yuntao Shou, Tao Meng, Haowen Chen, Keqin Li
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have proven the superiority of the GS-MCC architecture proposed in this paper on two benchmark data sets. |
| Researcher Affiliation | Academia | 1 College of Computer and Mathematics, Central South University of Forestry and Technology, 410004, China 2 College of Computer Science and Electronic Engineering, Hunan University, 410082, China 3 Department of Computer Science, State University of New York, 12561, USA |
| Pseudocode | No | The paper describes the methodology in text and mathematical formulas but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/Fuchen Zhang/GS-MCC. |
| Open Datasets | Yes | In our experiments, we used two benchmark multimodal datasets IEMOCAP (Busso et al. 2008) and MELD (Poria et al. 2019), which are widely used in multimodal emotion recognition. |
| Dataset Splits | Yes | The optimal parameters of all models were obtained by performing parameter adjustment using the leave-one-out cross-validation method on the validation set. |
| Hardware Specification | Yes | All experiments are conducted using Python 3.8 and Py Torch 1.8 deep learning framework and performed on a single NVIDIA RTX 4090 24G GPU. |
| Software Dependencies | Yes | All experiments are conducted using Python 3.8 and Py Torch 1.8 deep learning framework |
| Experiment Setup | Yes | Our model is trained using Adam W with a learning rate of 1e5, cross-entropy as the loss function, and a batch size of 32. |