arXiv:2109.08820
공식 코드 · Papers with Code
Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems
원문 초록
Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs. However, users may have requests that are out of the scope of these APIs. This work focuses on identifying such user requests. Existing methods for this task mainly rely on fine-tuning pre-trained models on large annotated data. We propose a novel method, REDE, based on adaptive representation learning and density estimation. REDE can be applied to zero-shot cases, and quickly learns a high-performing detector with only a few shots by updating less than 3K parameters. We demonstrate REDE's competitive performance on DSTC9 data and our newly collected test set.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis
Dual Dimensions Geometric Representation Learning Based Document Dewarping
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
Molecular Machine Learning Using Euler Characteristic Transforms
Latent Thermodynamic Flows: Unified Representation Learning and Generative Modeling of Temperature-Dependent Behaviors from Limited Data
토론
이 논문과 연결된 포럼 글이 아직 없습니다.