Papers › Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling
Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling
Alireza Mohammadshahi, James Henderson
Recent models have shown that incorporating syntactic knowledge into the semantic role labelling (SRL) task leads to a significant improvement. In this paper, we propose Syntax-aware Graph-to-Graph Transformer (SynG2G-Tr) model, which encodes the syntactic structure using a novel way to input graph relations as embeddings, directly into the self-attention mechanism of Transformer. This approach adds a soft bias towards attention patterns that follow the syntactic structure but also allows the model to use this information to learn alternative patterns. We evaluate our model on both span-based and dependency-based SRL datasets, and outperform previous alternative methods in both in-domain and out-of-domain settings, on CoNLL 2005 and CoNLL 2009 datasets.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Role Labeling | CoNLL 2005 | Mohammadshahi and Henderson (2021) | F1 | 88.93 | #6 of 15 | Archive leaderboard | report |
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