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            <namePart type="given">Naoaki</namePart>
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    <abstract>Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text. Current approaches are based on autoregressive language models such as BART or T5, fine-tuned through Teacher Forcing to obtain a linearized version of the AMR graph from a sentence. In this paper, we present LeakDistill, a model and method that explores a modification to the Transformer architecture, using structural adapters to explicitly incorporate graph information into the learned representations and improve AMR parsing performance. Our experiments show how, by employing word-to-node alignment to embed graph structural information into the encoder at training time, we can obtain state-of-the-art AMR parsing through self-knowledge distillation, even without the use of additional data. We release the code at [http://www.github.com/sapienzanlp/LeakDistill](http://www.github.com/sapienzanlp/LeakDistill).</abstract>
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%0 Conference Proceedings
%T Incorporating Graph Information in Transformer-based AMR Parsing
%A Vasylenko, Pavlo
%A Huguet Cabot, Pere Lluís
%A Martínez Lorenzo, Abelardo Carlos
%A Navigli, Roberto
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F vasylenko-etal-2023-incorporating
%X Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text. Current approaches are based on autoregressive language models such as BART or T5, fine-tuned through Teacher Forcing to obtain a linearized version of the AMR graph from a sentence. In this paper, we present LeakDistill, a model and method that explores a modification to the Transformer architecture, using structural adapters to explicitly incorporate graph information into the learned representations and improve AMR parsing performance. Our experiments show how, by employing word-to-node alignment to embed graph structural information into the encoder at training time, we can obtain state-of-the-art AMR parsing through self-knowledge distillation, even without the use of additional data. We release the code at [http://www.github.com/sapienzanlp/LeakDistill](http://www.github.com/sapienzanlp/LeakDistill).
%R 10.18653/v1/2023.findings-acl.125
%U https://aclanthology.org/2023.findings-acl.125
%U https://doi.org/10.18653/v1/2023.findings-acl.125
%P 1995-2011

Markdown (Informal)

[Incorporating Graph Information in Transformer-based AMR Parsing](https://aclanthology.org/2023.findings-acl.125) (Vasylenko et al., Findings 2023)

• Incorporating Graph Information in Transformer-based AMR Parsing (Vasylenko et al., Findings 2023)

ACL

• Pavlo Vasylenko, Pere Lluís Huguet Cabot, Abelardo Carlos Martínez Lorenzo, and Roberto Navigli. 2023. Incorporating Graph Information in Transformer-based AMR Parsing. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1995–2011, Toronto, Canada. Association for Computational Linguistics.