# Syntax-aware Neural Semantic Role Labeling with Supertags

arXiv: https://arxiv.org/abs/1903.05260 · HTML: https://mlsanity.com/paper/1903.05260/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling](https://mlsanity.com/paper/2104.07704/) (2021)
- [An MRC Framework for Semantic Role Labeling](https://mlsanity.com/paper/2109.06660/) (2021)
- [Transition-based Semantic Role Labeling with Pointer Networks](https://mlsanity.com/paper/2205.10023/) (2022)
- [Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph](https://mlsanity.com/paper/2112.00503/) (2021)
- [Improve Retrieval-based Dialogue System via Syntax-Informed Attention](https://mlsanity.com/paper/2303.06605/) (2023)
- [NLPre: a revised approach towards language-centric benchmarking of Natural Language Preprocessing systems](https://mlsanity.com/paper/2403.04507/) (2024)

## Abstract (arXiv)

We introduce a new syntax-aware model for dependency-based semantic role labeling that outperforms syntax-agnostic models for English and Spanish. We use a BiLSTM to tag the text with supertags extracted from dependency parses, and we feed these supertags, along with words and parts of speech, into a deep highway BiLSTM for semantic role labeling. Our model combines the strengths of earlier models that performed SRL on the basis of a full dependency parse with more recent models that use no syntactic information at all. Our local and non-ensemble model achieves state-of-the-art performance on the CoNLL 09 English and Spanish datasets. SRL models benefit from syntactic information, and we show that supertagging is a simple, powerful, and robust way to incorporate syntax into a neural SRL system.