Polyglot Semantic Role Labeling
Not on the map. Why?
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 May 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 2; 2024: 1.
5 papers.
- 2023Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinationscited by 63
- 2023Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extractioncited by 6
- 2022Transition-based Semantic Role Labeling with Pointer Networkscited by 3
- 2024DAHRS: Divergence-Aware Hallucination-Remediated SRL Projectioncited by 3
- 2022A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysiscited by 0
- 2024DAHRS: Divergence-Aware Hallucination-Remediated SRL Projection12 Jul 2024
- 2023Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction20 May 2023
- 2023Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations31 Mar 2023
- 2022A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis31 May 2022
- 2022Transition-based Semantic Role Labeling with Pointer Networks20 May 2022