Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

Simone Conia, Andrea Bacciu, Roberto Navigli


Abstract
While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques. We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios. Most interestingly, our unified model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages. We release our code and model at https://github.com/SapienzaNLP/unify-srl.
Anthology ID:
2021.naacl-main.31
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
338–351
Language:
URL:
https://aclanthology.org/2021.naacl-main.31
DOI:
10.18653/v1/2021.naacl-main.31
Award:
 Outstanding Long Paper
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.31.pdf