A research dataset containing performance metrics for a multimodal conversational agent named EAC-Agent. The dataset likely contains results from validation on benchmark datasets IEMOCAP and MELD. It was uploaded by Shahid Jamil to figshare on 2026-04-17.
Use Cases
- Benchmarking emotion recognition models based on reported accuracy scores of 76.27% and 67.57%
- Evaluating response generation quality based on reported perplexity, BLEU, and ROUGE-L scores
- Comparing multimodal attention mechanisms based on the described self and cross-modal attention approach
Strengths
- Performance metrics are explicitly reported, including accuracy, perplexity, BLEU, and ROUGE-L scores
- The dataset is validated against two established benchmark datasets: IEMOCAP and MELD
Limitations
- The dataset is only 5.5 KB, indicating a very limited scope likely containing only summary results
- Column-level documentation is absent; field semantics must be inferred after download
- Row count is unknown, which may limit suitability assessment
Provenance
- Source
- figshare
- Freshness
- Last updated 2026-04-17 17:35:21