Abstract
Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between high-resource (e.g., English) and low-resource (e.g., German) settings. To overcome this issue, we propose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. Our approach outperforms the state of the art in all the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of training data is available. Our objective is not to reject approaches that rely on syntax, rather to set a strong and consistent language-independent baseline for future innovations in Semantic Role Labeling. We release our model code and checkpoints at https://github.com/SapienzaNLP/multi-srl.- Anthology ID:
- 2020.coling-main.120
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1396–1410
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.120
- DOI:
- 10.18653/v1/2020.coling-main.120
- Cite (ACL):
- Simone Conia and Roberto Navigli. 2020. Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1396–1410, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach (Conia & Navigli, COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.120.pdf
- Code
- sapienzanlp/multi-srl
- Data
- CoNLL-2012