With the increasing sophistication of cyber threats, the integration of machine learning (ML)
techniques in network security has become imperative for detecting and mitigating evolving …

State-of-the-art parametric and non-parametric style transfer approaches are prone to either
distorted local style patterns due to global statistics alignment, or unpleasing artifacts …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Abstract Knowledge distillation (KD) is the preliminary step for training non-autoregressive
translation (NAT) models, which eases the training of NAT models at the cost of losing …

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To
better comprehend long complicated sentences and obtain accurate aspect-specific …

Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT)
models by reducing the complexity of the raw data with an autoregressive teacher model. In …

The intersection of quantum computing and artificial intelligence (AI) within the cloud
environment represents a paradigm shift in the capabilities of computational technologies …

We present a simple and effective pretraining strategy--bidirectional training (BiT) for neural
machine translation. Specifically, we bidirectionally update the model parameters at the …

Slot filling and intent detection are two main tasks in spoken language understanding (SLU)
system. In this paper, we propose a novel non-autoregressive model named SlotRefine for …

Knowledge distillation (KD) is commonly used to construct synthetic data for training non-
autoregressive translation (NAT) models. However, there exists a discrepancy on low …