Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling
| Type of publication: | Conference paper |
| Citation: | Mohammadshahi_ARXIV_2021 |
| Booktitle: | Arxiv |
| Year: | 2021 |
| Month: | April |
| Abstract: | The goal of semantic role labelling (SRL) is to recognise the predicate-argument structure of a sentence. Recent models have shown that syntactic information can enhance the SRL performance, but other syntax-agnostic approaches achieved reasonable performance. The best way to encode syntactic information for the SRL task is still an open question. In this paper, we propose the Syntax-aware Graph-to-Graph Transformer (SynG2G-Tr) architecture, which encodes the syntactic structure with 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 dependency-based and span-based SRL datasets, and outperform all previous syntax-aware and syntax-agnostic models in both in-domain and out-of-domain settings, on the CoNLL 2005 and CoNLL 2009 datasets. Our architecture is general and can be applied to encode any graph information for a desired downstream task. |
| Keywords: | |
| Projects: |
Idiap Intrepid |
| Authors: | |
| Crossref by |
Mohammadshahi_REP4NLPATACL2023_2023 |
| Added by: | [UNK] |
| Total mark: | 0 |
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