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        <title>Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models</title>
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    <abstract>Syntactic Transformer language models aim to achieve better generalization through simultaneously modeling syntax trees and sentences. While prior work has been focusing on adding constituency-based structures to Transformers, we introduce Dependency Transformer Grammars (DTGs), a new class of Transformer language model with explicit dependency-based inductive bias. DTGs simulate dependency transition systems with constrained attention patterns by modifying attention masks, incorporate the stack information through relative positional encoding, and augment dependency arc representation with a combination of token embeddings and operation embeddings. When trained on a dataset of sentences annotated with dependency trees, DTGs achieve better generalization while maintaining comparable perplexity with Transformer language model baselines. DTGs also outperform recent constituency-based models, showing that dependency can better guide Transformer language models. Our code is released at https://github.com/zhaoyd1/Dep_Transformer_Grammars.</abstract>
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%0 Conference Proceedings
%T Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models
%A Zhao, Yida
%A Lou, Chao
%A Tu, Kewei
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhao-etal-2024-dependency
%X Syntactic Transformer language models aim to achieve better generalization through simultaneously modeling syntax trees and sentences. While prior work has been focusing on adding constituency-based structures to Transformers, we introduce Dependency Transformer Grammars (DTGs), a new class of Transformer language model with explicit dependency-based inductive bias. DTGs simulate dependency transition systems with constrained attention patterns by modifying attention masks, incorporate the stack information through relative positional encoding, and augment dependency arc representation with a combination of token embeddings and operation embeddings. When trained on a dataset of sentences annotated with dependency trees, DTGs achieve better generalization while maintaining comparable perplexity with Transformer language model baselines. DTGs also outperform recent constituency-based models, showing that dependency can better guide Transformer language models. Our code is released at https://github.com/zhaoyd1/Dep_Transformer_Grammars.
%U https://aclanthology.org/2024.acl-long.84
%P 1543-1556

Markdown (Informal)

[Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models](https://aclanthology.org/2024.acl-long.84) (Zhao et al., ACL 2024)

• Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models (Zhao et al., ACL 2024)

ACL

• Yida Zhao, Chao Lou, and Kewei Tu. 2024. Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1543–1556, Bangkok, Thailand. Association for Computational Linguistics.