InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles

Simone Conia, Fabrizio Brignone, Davide Zanfardino, Roberto Navigli

Abstract
Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts. To address this issue we present a new platform named Intelligible Verbs and Roles (InVeRo). This platform provides access to a new verb resource, VerbAtlas, and a state-of-the-art pretrained implementation of a neural, span-based architecture for SRL. Both the resource and the system provide human-readable verb sense and semantic role information, with an easy to use Web interface and RESTful APIs available at http://nlp.uniroma1.it/invero.
Anthology ID:
2020.emnlp-demos.11
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
October
Year:
2020
Address:
Online
Editors:
Qun Liu, David Schlangen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
77–84
Language:
URL:
https://aclanthology.org/2020.emnlp-demos.11
DOI:
10.18653/v1/2020.emnlp-demos.11
Bibkey:
Cite (ACL):
Simone Conia, Fabrizio Brignone, Davide Zanfardino, and Roberto Navigli. 2020. InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 77–84, Online. Association for Computational Linguistics.
Cite (Informal):
InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles (Conia et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.emnlp-demos.11.pdf
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