Search Results for author:
Found 5 papers, 0 papers with code
Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot learning for Dialogue State Tracking (DST) focuses on generalizing to an unseen domain without the expense of collecting in domain data.
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios
However, existing methods overlook the non-independent and identically distributed (non-IDD) issue stemming from multidomain brain connectivity heterogeneity, in which data domains are drawn from different hospitals and imaging modalities.
DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion
Human de-occlusion, which aims to infer the appearance of invisible human parts from an occluded image, has great value in many human-related tasks, such as person re-id, and intention inference.
Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models
We evaluate our method on both open and closed LLMs, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation.
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking
Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle a variety of task-oriented dialogue domains without the cost of collecting in-domain data.
