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 [BibTeX] [Marc21]
Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling
Type of publication: Conference paper
Citation: Mohammadshahi_REP4NLPATACL2023_2023
Booktitle: Procceedings of 8th Workshop on Representation Learning for NLP
Year: 2023
Month: July
Crossref: Mohammadshahi_ARXIV_2021:
URL: https://arxiv.org/abs/2104.077...
Abstract: 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.
Authors Mohammadshahi, Alireza
Henderson, James
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Total mark: 0