EAC-Agent Benchmark Performance Statistics on IEMOCAP and MELD

5.5 KB of tabular data contains benchmark performance statistics for the EAC-Agent multimodal conversational model. Shahid Jamil published the dataset on figshare in April 2026. The data likely includes accuracy, perplexity, BLEU, and ROUGE-L scores for emotion recognition and response generation.

Use Cases

• Benchmarking new multimodal chatbot models based on reported accuracy and perplexity scores
• Comparing emotion recognition performance across datasets based on the reported 76.27% and 67.57% accuracy figures
• Evaluating response generation quality based on the reported BLEU and ROUGE-L scores
• Analyzing the impact of multimodal attention mechanisms on conversational AI performance

Strengths

• Specific performance metrics are provided, including accuracy of 76.27% on IEMOCAP and 67.57% on MELD
• Model evaluation includes multiple metrics: perplexity, BLEU, and ROUGE-L scores
• Dataset is openly licensed under CC-BY-4.0

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset is very small (5.5 KB), indicating limited scope

Provenance

Source

figshare

Freshness

Last updated 2026-04-17 17:35:22; freshness should be verified