MELD Dataset: Multimodal Emotion Recognition and Response Generation Results

Shahid Jamil published results from a multimodal conversational agent model on April 17, 2026. The 5.5 KB Excel file likely contains performance metrics for emotion classification and response generation on the MELD benchmark dataset. The model incorporates text, audio, and visual features.

Use Cases

• Benchmarking emotion recognition models based on reported accuracy scores
• Evaluating chatbot response generation based on reported perplexity, BLEU, and ROUGE-L scores
• Comparing multimodal AI approaches based on the described text, audio, and visual feature integration

Strengths

• Performance metrics are provided, including an emotion recognition accuracy of 67.57% on MELD
• Response generation metrics include perplexity (42.30), BLEU (0.30), and ROUGE-L (0.44) scores on MELD
• 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), suggesting limited scope

Provenance

Source

figshare

Freshness

Last updated 2026-04-17 17:35:24; freshness should be verified