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arXiv · 2306.13467

Incorporating Graph Information in Transformer-based AMR Parsing

Pavlo Vasylenko·Pere-Lluís Huguet Cabot·Abelardo Carlos Martínez Lorenzo·Roberto Navigli
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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 \url{http://www.github.com/sapienzanlp/LeakDistill}.

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Pavlo Vasylenko, Pere-Lluís Huguet Cabot, Abelardo Carlos Martínez Lorenzo, Roberto Navigli. 2023-06-23. Incorporating Graph Information in Transformer-based AMR Parsing. https://arxiv.org/abs/2306.13467

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