Weighting Model Based on Group Dynamics to Measure Convergence in Multi-party Dialogue
This paper proposes a new weighting method for extending a dyad-level measure of convergence to multi-party dialogues by considering group dynamics instead of simply averaging. Experiments indicate the usefulness of the proposed weighted measure and also show that in general a proper weighting of the dyad-level measures performs better than non-weighted averaging in multiple tasks.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Grouped Adaptive Loss Weighting for Person Search
Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person search is a typical multi-task learning prob…
Model OptimizationMulti-Task LearningPerson Re-IdentificationPerson SearchA Self-Attentive Meta-Optimizer with Group-Adaptive Learning Rates and Weight Decay
Adaptive optimizers like AdamW apply uniform hyperparameters across all parameter groups, ignoring heterogeneous optimization dynamics across layers and modules. We address this limitation by proposing MetaAdamW - a new …
Time Series ForecastingImage ClassificationMachine TranslationSentiment AnalysisDEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents
Accurately modeling opinion change through social interactions is crucial for understanding and mitigating polarization, misinformation, and societal conflict. Recent work simulates opinion dynamics with role-playing LLM…
Learning to Align, Aligning to Learn: A Unified Approach for Self-Optimized Alignment
Alignment methodologies have emerged as a critical pathway for enhancing language model alignment capabilities. While SFT (supervised fine-tuning) accelerates convergence through direct token-level loss intervention, its…
Reinforcement LearningUnderstanding Difficulty-based Sample Weighting with a Universal Difficulty Measure
Sample weighting is widely used in deep learning. A large number of weighting methods essentially utilize the learning difficulty of training samples to calculate their weights. In this study, this scheme is called diffi…
Deep Learning