Papers › Incorporating Graph Information in Transformer-based AMR Parsing
Incorporating Graph Information in Transformer-based AMR Parsing
Pavlo Vasylenko, Pere-Lluís Huguet Cabot, Abelardo Carlos Martínez Lorenzo, Roberto Navigli
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| AMR Parsing | LDC2017T10 | LeakDistill | Smatch | 86.1 | #3 of 27 | Archive leaderboard | report |
| AMR Parsing | LDC2017T10 | LeakDistill (base) | Smatch | 84.7 | #10 of 27 | Archive leaderboard | report |
| AMR Parsing | LDC2020T02 | LeakDistill | Smatch | 84.6 | #3 of 13 | Archive leaderboard | report |
| AMR Parsing | LDC2020T02 | LeakDistill (base) | Smatch | 83.5 | #10 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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