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This research investigates how linearizing syntactic trees in Transformer Grammars affects model performance by comparing Depth-First, Breadth-First, and hybrid traversal strategies. The study empirically evaluates these methods across language modeling and summarization tasks to reveal trade-offs between structural lookahead and early lexical generation for task-aware design.
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Does Traversal Order Matter? A Systematic Study of Tree Traversal Methods in Transformer Grammars

arXiv2 min read

arXiv:2606.16836v1 Announce Type: new Abstract: Transformer Grammars (TGs) enhance language modeling by incorporating syntactic tree structures. Despite the potentially significant impact on model performance of how syntactic trees are linearized in TGs, existing studies rely solely on Depth-First Traversal (DFT) for linearization. In this paper, we expand the traversal design space by exploring Breadth-First Traversal (BFT) and a novel hybrid traversal strategy, Production-Rule Traversal (PRT), which combines the structural lookahead of BFT with the early lexical generation of DFT. We integrate these traversal methods with varying tree configurations and masking strategies, and empirically evaluate their performance on language modeling, syntactic generalization and summarization. We reveal the inherent trade-offs between nested composition and global lookahead, providing actionable recommendations for designing task-aware Transformer Grammars.

Title:Does Traversal Order Matter? A Systematic Study of Tree Traversal Methods in Transformer Grammars

Abstract:Transformer Grammars (TGs) enhance language modeling by incorporating syntactic tree structures. Despite the potentially significant impact on model performance of how syntactic trees are linearized in TGs, existing studies rely solely on Depth-First Traversal (DFT) for linearization. In this paper, we expand the traversal design space by exploring Breadth-First Traversal (BFT) and a novel hybrid traversal strategy, Production-Rule Traversal (PRT), which combines the structural lookahead of BFT with the early lexical generation of DFT. We integrate these traversal methods with varying tree configurations and masking strategies, and empirically evaluate their performance on language modeling, syntactic generalization and summarization. We reveal the inherent trade-offs between nested composition and global lookahead, providing actionable recommendations for designing task-aware Transformer Grammars.

Computation and Language (cs.CL)

arXiv:2606.16836 [cs.CL]

(or arXiv:2606.16836v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.16836 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 226 words

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