Search Results for author:
Found 3 papers, 2 papers with code
Asynchronous and Segmented Bidirectional Encoding for NMT
With the rapid advancement of Neural Machine Translation (NMT), enhancing translation efficiency and quality has become a focal point of research.
Efficient Reinforcemen Learning via Decoupling Exploration and Utilization
OPARL employs a unique dual-actor approach: an optimistic actor dedicated to exploration and a pessimistic actor focused on utilization, thereby effectively differentiating between exploration and utilization strategies.
Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona
We design a Contrastive Latent Variable-based model (CLV) that clusters the dense persona descriptions into sparse categories, which are combined with the history query to generate personalized responses.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
