@inproceedings{xie-etal-2023-mixtea,
title = "{M}ix{TEA}: Semi-supervised Entity Alignment with Mixture Teaching",
author = "Xie, Feng and
Song, Xin and
Zeng, Xiang and
Zhao, Xuechen and
Tian, Lei and
Zhou, Bin and
Tan, Yusong",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.63",
doi = "10.18653/v1/2023.findings-emnlp.63",
pages = "886--896",
abstract = "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.",
}
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%0 Conference Proceedings
%T MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
%A Xie, Feng
%A Song, Xin
%A Zeng, Xiang
%A Zhao, Xuechen
%A Tian, Lei
%A Zhou, Bin
%A Tan, Yusong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F xie-etal-2023-mixtea
%X 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.
%R 10.18653/v1/2023.findings-emnlp.63
%U https://aclanthology.org/2023.findings-emnlp.63
%U https://doi.org/10.18653/v1/2023.findings-emnlp.63
%P 886-896
Markdown (Informal)
[MixTEA: Semi-supervised Entity Alignment with Mixture Teaching](https://aclanthology.org/2023.findings-emnlp.63) (Xie et al., Findings 2023)
ACL