SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities

The rapid advance in artificial intelligence technology has facilitated the prosperity of digital humanities research. Against such backdrop, research methods need to be transformed in the intelligent processing of ancient texts, which is a crucial component of digital humanities research, so as to adapt to new development trends in the wave of AIGC. In this study, we propose a GPT model called SikuGPT based on the corpus of Siku Quanshu. The model's performance in tasks such as intralingual translation and text classification exceeds that of other GPT-type models aimed at processing ancient texts. SikuGPT's ability to process traditional Chinese ancient texts can help promote the organization of ancient information and knowledge services, as well as the international dissemination of Chinese ancient culture.

DeepE: a deep neural network for knowledge graph embedding

Recently, neural network based methods have shown their power in learning more expressive features on the task of knowledge graph embedding (KGE). However, the performance of deep methods often falls behind the shallow ones on simple graphs. One possible reason is that deep models are difficult to train, while shallow models might suffice for accurately representing the structure of the simple KGs. In this paper, we propose a neural network based model, named DeepE, to address the problem, which stacks multiple building blocks to predict the tail entity based on the head entity and the relation. Each building block is an addition of a linear and a non-linear function. The stacked building blocks are equivalent to a group of learning functions with different non-linear depth. Hence, DeepE allows deep functions to learn deep features, and shallow functions to learn shallow features. Through extensive experiments, we find DeepE outperforms other state-of-the-art baseline methods. A major advantage of DeepE is the robustness. DeepE achieves a Mean Rank (MR) score that is 6%, 30%, 65% lower than the best baseline methods on FB15k-237, WN18RR and YAGO3-10. Our design makes it possible to train much deeper networks on KGE, e.g. 40 layers on FB15k-237, and without scarifying precision on simple relations.