# Syntax Role for Neural Semantic Role Labeling

arXiv: https://arxiv.org/abs/2009.05737 · HTML: https://mlsanity.com/paper/2009.05737/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments](https://mlsanity.com/paper/2110.06865/) (2021)
- [Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model](https://mlsanity.com/paper/2111.09634/) (2021)
- [Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling](https://mlsanity.com/paper/2104.07704/) (2021)
- [Transition-based Semantic Role Labeling with Pointer Networks](https://mlsanity.com/paper/2205.10023/) (2022)
- [Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data](https://mlsanity.com/paper/2306.09697/) (2023)
- [Information Extraction in Domain and Generic Documents: Findings from Heuristic-based and Data-driven Approaches](https://mlsanity.com/paper/2307.00130/) (2023)
- [Dynamic Named Entity Recognition](https://mlsanity.com/paper/2302.10314/) (2023)

## Abstract (arXiv)

Semantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument structure of a sentence. Previous studies in terms of traditional models have shown syntactic information can make remarkable contributions to SRL performance; however, the necessity of syntactic information was challenged by a few recent neural SRL studies that demonstrate impressive performance without syntactic backbones and suggest that syntax information becomes much less important for neural semantic role labeling, especially when paired with recent deep neural network and large-scale pre-trained language models. Despite this notion, the neural SRL field still lacks a systematic and full investigation on the relevance of syntactic information in SRL, for both dependency and both monolingual and multilingual settings. This paper intends to quantify the importance of syntactic information for neural SRL in the deep learning framework. We introduce three typical SRL frameworks (baselines), sequence-based, tree-based, and graph-based, which are accompanied by two categories of exploiting syntactic information: syntax pruning-based and syntax feature-based. Experiments are conducted on the CoNLL-2005, 2009, and 2012 benchmarks for all languages available, and results show that neural SRL models can still benefit from syntactic information under certain conditions. Furthermore, we show the quantitative significance of syntax to neural SRL models together with a thorough empirical survey using existing models.