4 accepted papers
Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. Previous works typically employ a sequence-to-sequence framework to generate medical responses by modeling dialogue context as sequential text with annotated medical entities. While these method…
Electroencephalography (EEG) is crucial for recording brain activity, with applications in medicine, neuroscience, and brain-computer interfaces (BCI). However, challenges such as low signal-to-noise ratio (SNR), high inter-subject variability, and channel mismatch complicate the extraction of…
Since rain shows a variety of shapes and directions, learning the degradation representation is extremely challenging for single image deraining. Existing methods mainly propose to designing complicated modules to implicitly learn latent degradation representation from rainy images. However, it is h…
End-to-end Task-oriented spoken dialog systems typically require modeling two types of inputs, namely, the dialog history which is a sequence of utterances and the knowledge base (KB) associated with the dialog history. While modeling these inputs, current state-of-the-art models typically ignore th…