MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
Feng Xie Xin Song Xiang Zeng Xuechen Zhao Lei Tian Bin Zhou Yusong Tan
Semi-supervised entity alignment (EA) is a practical and challenging task
because of the lack of adequate labeled mappings as training data. Most works
address this problem by generating pseudo mappings for unlabeled entities.
However, they either suffer from the erroneous (noisy) pseudo mappings or
largely ignore the uncertainty of pseudo mappings. In this paper, we propose a
novel semi-supervised EA method, termed as MixTEA, which guides the model
learning with an end-to-end mixture teaching of manually labeled mappings and
probabilistic pseudo mappings. We firstly train a student model using few
labeled mappings as standard. More importantly, in pseudo mapping learning, we
propose a bi-directional voting (BDV) strategy that fuses the alignment
decisions in different directions to estimate the uncertainty via the joint
matching confidence score. Meanwhile, we also design a matching diversity-based
rectification (MDR) module to adjust the pseudo mapping learning, thus reducing
the negative influence of noisy mappings. Extensive results on benchmark
datasets as well as further analyses demonstrate the superiority and the
effectiveness of our proposed method.