Computer Science > Machine Learning
[Submitted on 11 Oct 2022 (v1), last revised 10 Mar 2023 (this version, v3)]
Title:Relational Attention: Generalizing Transformers for Graph-Structured Tasks
View PDFAbstract:Transformers flexibly operate over sets of real-valued vectors representing task-specific entities and their attributes, where each vector might encode one word-piece token and its position in a sequence, or some piece of information that carries no position at all. But as set processors, transformers are at a disadvantage in reasoning over more general graph-structured data where nodes represent entities and edges represent relations between entities. To address this shortcoming, we generalize transformer attention to consider and update edge vectors in each transformer layer. We evaluate this relational transformer on a diverse array of graph-structured tasks, including the large and challenging CLRS Algorithmic Reasoning Benchmark. There, it dramatically outperforms state-of-the-art graph neural networks expressly designed to reason over graph-structured data. Our analysis demonstrates that these gains are attributable to relational attention's inherent ability to leverage the greater expressivity of graphs over sets.
Submission history
From: Cameron Diao [view email][v1] Tue, 11 Oct 2022 00:25:04 UTC (927 KB)
[v2] Mon, 21 Nov 2022 20:31:26 UTC (1,129 KB)
[v3] Fri, 10 Mar 2023 19:59:03 UTC (1,163 KB)
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