Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation

Abelardo Carlos Martínez Lorenzo, Marco Maru, Roberto Navigli


Abstract
A language-independent representation of meaning is one of the most coveted dreams in Natural Language Understanding. With this goal in mind, several formalisms have been proposed as frameworks for meaning representation in Semantic Parsing. And yet, the dependencies these formalisms share with respect to language-specific repositories of knowledge make the objective of closing the gap between high- and low-resourced languages hard to accomplish. In this paper, we present the BabelNet Meaning Representation (BMR), an interlingual formalism that abstracts away from language-specific constraints by taking advantage of the multilingual semantic resources of BabelNet and VerbAtlas. We describe the rationale behind the creation of BMR and put forward BMR 1.0, a dataset labeled entirely according to the new formalism. Moreover, we show how BMR is able to outperform previous formalisms thanks to its fully-semantic framing, which enables top-notch multilingual parsing and generation. We release the code at https://github.com/SapienzaNLP/bmr.
Anthology ID:
2022.acl-long.121
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1727–1741
Language:
URL:
https://aclanthology.org/2022.acl-long.121
DOI:
10.18653/v1/2022.acl-long.121
Bibkey:
Cite (ACL):
Abelardo Carlos Martínez Lorenzo, Marco Maru, and Roberto Navigli. 2022. Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1727–1741, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Fully-Semantic Parsing and Generation: the BabelNet Meaning Representation (Martínez Lorenzo et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.121.pdf
Code
 sapienzanlp/bmr