Semi-supervised learning provides an expressive framework for exploiting
unlabeled data when labels are insufficient. Previous semi-supervised learning
methods typically match model predictions of different data-augmented views in
a single-level interaction manner, which highly relies on the quality of
pseudo-labels and results in semi-supervised learning not robust. In this
paper, we propose a novel SSL method called DualMatch, in which the class
prediction jointly invokes feature embedding in a dual-level interaction
manner. DualMatch requires consistent regularizations for data augmentation,
specifically, 1) ensuring that different augmented views are regulated with
consistent class predictions, and 2) ensuring that different data of one class
are regulated with similar feature embeddings. Extensive experiments
demonstrate the effectiveness of DualMatch. In the standard SSL setting, the
proposal achieves 9% error reduction compared with SOTA methods, even in a more
challenging class-imbalanced setting, the proposal can still achieve 6% error
reduction. Code is available at this https URL

提出了一种名为 DualMatch 的新型半监督学习方法，其中类别预测以双层交互方式调用特征嵌入，通过一致的正则化确保不同的增强视图受到一致的类别预测约束，并确保同一类别的不同数据具有类似的特征嵌入，实验证明了 DualMatch 的有效性。

DualMatch：具有双层交互的鲁棒半监督学习

DualMatch: Robust Semi-Supervised Learning with Dual-Level Interaction

Entity alignment (EA) is a fundamental data integration task that identifies
equivalent entities between different knowledge graphs (KGs). Temporal
Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing
timestamps, which have received increasing attention. State-of-the-art
time-aware EA studies have suggested that the temporal information of TKGs
facilitates the performance of EA. However, existing studies have not
thoroughly exploited the advantages of temporal information in TKGs. Also, they
perform EA by pre-aligning entity pairs, which can be labor-intensive and thus
inefficient.
In this paper, we present DualMatch which effectively fuses the relational
and temporal information for EA. DualMatch transfers EA on TKGs into a weighted
graph matching problem. More specifically, DualMatch is equipped with an
unsupervised method, which achieves EA without necessitating seed alignment.
DualMatch has two steps: (i) encoding temporal and relational information into
embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii)
fusing both information and transforming it into alignment using a novel
graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on
TKGs with or without supervision, due to its capability of effectively
capturing temporal information. Extensive experiments on three real-world TKG
datasets offer the insight that DualMatch outperforms the state-of-the-art
methods in terms of H@1 by 2.4% - 10.7% and MRR by 1.7% - 7.6%, respectively.

本文介绍了一种名为 DualMatch 的方法，这种方法在实体对齐中有效地融合了关系和时间信息，并将其转化为一种基于加权图匹配的解码器。DualMatch 具有无监督方法和能够有效捕获时间信息等优点，能够在有监督和无监督的情况下执行。实验结果表明，Dual Match 在三个真实世界的时间知识图数据集上表现优于现有的方法。