This paper investigates cross-lingual temporal knowledge graph reasoning
problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs)
in low-resource languages by transfering knowledge from TKGs in high-resource
ones. The cross-lingual distillation ability across TKGs becomes increasingly
crucial, in light of the unsatisfying performance of existing reasoning methods
on those severely incomplete TKGs, especially in low-resource languages.
However, it poses tremendous challenges in two aspects. First, the
cross-lingual alignments, which serve as bridges for knowledge transfer, are
usually too scarce to transfer sufficient knowledge between two TKGs. Second,
temporal knowledge discrepancy of the aligned entities, especially when
alignments are unreliable, can mislead the knowledge distillation process. We
correspondingly propose a mutually-paced knowledge distillation model MP-KD,
where a teacher network trained on a source TKG can guide the training of a
student network on target TKGs with an alignment module. Concretely, to deal
with the scarcity issue, MP-KD generates pseudo alignments between TKGs based
on the temporal information extracted by our representation module. To maximize
the efficacy of knowledge transfer and control the noise caused by the temporal
knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention
mechanism to dynamically estimate the alignment strength. The two procedures
are mutually paced along with model training. Extensive experiments on twelve
cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the
effectiveness of the proposed MP-KD method.

该研究探讨了跨语言时间知识图谱推理问题，并提出了相应的相互加速的知识蒸馏模型，用于解决在低资源语言下推理性能不佳的问题。