# Polyglot Semantic Role Labeling

arXiv: https://arxiv.org/abs/1805.11598 · HTML: https://mlsanity.com/paper/1805.11598/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 5 of 5)

- [Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations](https://mlsanity.com/paper/2303.18027/) (2023)
- [Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction](https://mlsanity.com/paper/2305.12258/) (2023)
- [Transition-based Semantic Role Labeling with Pointer Networks](https://mlsanity.com/paper/2205.10023/) (2022)
- [DAHRS: Divergence-Aware Hallucination-Remediated SRL Projection](https://mlsanity.com/paper/2407.09283/) (2024)
- [A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis](https://mlsanity.com/paper/2205.15514/) (2022)

## Abstract (arXiv)

Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages. We experiment with a new approach where we combine resources from a pair of languages in the CoNLL 2009 shared task to build a polyglot semantic role labeler. Notwithstanding the absence of parallel data, and the dissimilarity in annotations between languages, our approach results in an improvement in SRL performance on multiple languages over a monolingual baseline. Analysis of the polyglot model shows it to be advantageous in lower-resource settings.