Research
Areas
Explore our core research activities and methodologies driving the advancement of conversational AI understanding.
Dataset Collection
We curate comprehensive conversation datasets from diverse sources, ensuring representation across domains, languages, and interaction patterns. Our collection methodology prioritizes quality, diversity, and ethical sourcing.
- Multi-source data aggregation
- Privacy-preserving collection methods
- Domain-specific corpus development
- Cross-lingual dataset creation
Data Annotation
Precise labeling and categorization of dialogue patterns, semantic structures, and conversational intents. Our annotation framework enables fine-grained understanding of human-AI interactions.
- Intent and entity labeling
- Dialogue act classification
- Sentiment and tone analysis
- Multi-turn context annotation
Model Training
Fine-tuning language models with specialized conversation understanding capabilities. We develop training methodologies that enhance contextual comprehension and response generation.
- Custom fine-tuning pipelines
- Multi-task learning approaches
- Efficient training strategies
- Model compression techniques
Conversation Analysis
Deep analysis of dialogue flows, intent patterns, and conversational dynamics. We extract actionable insights from conversation data to improve AI system design.
- Turn-taking pattern analysis
- Topic flow visualization
- Coherence measurement
- Engagement metrics
Quality Evaluation
Rigorous assessment metrics for dataset quality and model performance. Our evaluation frameworks ensure reliability and reproducibility across research outcomes.
- Automated quality checks
- Human evaluation protocols
- Benchmark suite development
- Comparative analysis tools
Research Output
Publishing findings and contributing to the advancement of conversational AI through peer-reviewed papers, open-source tools, and community engagement.
- Peer-reviewed publications
- Open-source tooling
- Dataset releases
- Technical reports