We argue that Transformers are essentially graph-to-graph models, with
sequences just being a special case. Attention weights are functionally
equivalent to graph edges. Our Graph-to-Graph Transformer architecture makes
this ability explicit, by inputting graph edges into the attention weight
computations and predicting graph edges with attention-like functions, thereby
integrating explicit graphs into the latent graphs learned by pretrained
Transformers. Adding iterative graph refinement provides a joint embedding of
input, output, and latent graphs, allowing non-autoregressive graph prediction
to optimise the complete graph without any bespoke pipeline or decoding
strategy. Empirical results show that this architecture achieves
state-of-the-art accuracies for modelling a variety of linguistic structures,
integrating very effectively with the latent linguistic representations learned
by pretraining.

我们认为 Transformer 模型本质上是图到图的模型，序列只是一种特殊情况。注意力权重在功能上等价于图中的边。我们的图到图 Transformer 架构明确地表达了这个能力，通过将图的边作为输入用于注意力权重计算，并使用类似于注意力的函数预测图中的边，从而将显式图集成到预训练的 Transformer 模型中学习出的潜在图中。添加迭代的图优化过程提供了输入、输出和潜在图的联合嵌入，使得非自回归图预测能够优化完整图，无需任何专门的流水线或解码策略。实证结果表明，该架构在对各种语言结构建模方面取得了最先进的准确性，与预训练学习的潜在语言表示非常有效地集成在一起。

将 Transformer 模型作为图到图模型

Transformers as Graph-to-Graph Models

In the domain of semi-supervised learning, the current approaches
insufficiently exploit the potential of considering inter-instance
relationships among (un)labeled data. In this work, we address this limitation
by providing an approach for inferring latent graphs that capture the intrinsic
data relationships. By leveraging graph-based representations, our approach
facilitates the seamless propagation of information throughout the graph,
enabling the effective incorporation of global and local knowledge. Through
evaluations on biomedical tabular datasets, we compare the capabilities of our
approach to other contemporary methods. Our work demonstrates the significance
of inter-instance relationship discovery as practical means for constructing
robust latent graphs to enhance semi-supervised learning techniques. Our method
achieves state-of-the-art results on three biomedical datasets.

通过建立潜在图来发现数据之间的内在关系，我们的方法在半监督学习中取得了最先进的结果，并且在三个生物医学数据集上进行了验证。