# Syntax-Enhanced Self-Attention-Based Semantic Role Labeling

arXiv: https://arxiv.org/abs/1910.11204 · HTML: https://mlsanity.com/paper/1910.11204/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers](https://mlsanity.com/paper/2203.06958/) (2022)
- [End-to-end Semantic Role Labeling with Neural Transition-based Model](https://mlsanity.com/paper/2101.00394/) (2021)
- [Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training](https://mlsanity.com/paper/2104.08645/) (2021)
- [Does injecting linguistic structure into language models lead to better alignment with brain recordings?](https://mlsanity.com/paper/2101.12608/) (2021)
- [Syntax-augmented Multilingual BERT for Cross-lingual Transfer](https://mlsanity.com/paper/2106.02134/) (2021)
- [A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems](https://mlsanity.com/paper/2106.05830/) (2021)
- [Punctuation Restoration Improves Structure Understanding Without Supervision](https://mlsanity.com/paper/2402.08382/) (2024)
- [MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset](https://mlsanity.com/paper/2205.06703/) (2022)

## Abstract (arXiv)

As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of encoding the syntactic information derived from dependency trees of different quality and representations; we propose a syntax-enhanced self-attention model and compare it with other two strong baseline methods; and we conduct experiments with newly published deep contextualized word representations as well. The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.