EAC-Agent: Multimodal Chatbot Performance Metrics on IEMOCAP and MELD

Benchmark results from 2026 comparing a novel multimodal chatbot model against existing techniques. The dataset likely contains performance metrics for emotion classification and response generation, including accuracy, perplexity, BLEU, and ROUGE-L scores. It was authored by Shahid Jamil and uploaded to figshare.

Use Cases

• Benchmarking new multimodal chatbot models based on reported accuracy and perplexity scores
• Analyzing the performance of transformer-based sequence-to-sequence models on emotion recognition tasks
• Comparing emotion-aware response generation techniques based on BLEU and ROUGE-L metrics

Strengths

• Includes specific performance metrics: 76.27% accuracy on IEMOCAP and 67.57% on MELD
• Provides multiple evaluation scores for response generation: perplexity, BLEU, and ROUGE-L
• Results are validated on two established benchmark datasets

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 9.5 KB, indicating a very limited scope likely containing only summary results

Provenance

Source

figshare

Freshness

Last updated 2026-04-17 17:35:20