Abstract: The invention discloses an energy-efficient sample selection method based on sample complexity, which performs sample selection on the raw data sets through two stages of inter-class sampling and intra-class sampling, the object is to select representative samples from large-scale data sets, thereby reducing the number of samples used for model training and achieving the object of lightweight training. Compared with the prior art, the invention has the following advantages: the invention proposes an energy-efficient sample selection method based on complexity, selects representative samples from large-scale datasets for efficient model training, and proves that sample complexity and model training strategies have a very important impact on the efficient training of deep neural networks.

Abstract: The disclosure relates to a method for generating personalized dialogue content, in which an implicit association between personalized characteristics and corresponding dialogue replies is extracted by collecting a set of personalized dialogue data; a vector representation of a dialogue context and texts of the personalized characteristics is learned with a Transformer model; finally, through learning a sequence dependency between natural languages, a subsequent content may be automatically predicted and generated from a previous text, so that the generating of corresponding reply content may be achieved according to the dialogue context. With various optimization algorithms added, a generation probability of universal reply can be reduced and a diversity of the generated dialogue content can be improved.