Specifically, we obtain a group of images (PVIs) for each post based on a pre-trained word-image mapping model.
In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.
As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field.
The cross-subject application of EEG-based brain-computer interface (BCI) has always been limited by large individual difference and complex characteristics that are difficult to perceive.
Neural dialogue response generation has gained much popularity in recent years.
In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.
Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses.
For each conversation, the model generates parameters of the encoder-decoder by referring to the input context.
In contrast to previous work, KNPTC is able to integrate explicit knowledge into NMT for pinyin typo correction, and is able to learn to correct a variety of typos without the guidance of manually selected constraints or languagespecific features.