Neural Proof Nets

Konstantinos Kogkalidis, Michael Moortgat, Richard Moot


Abstract
Linear logic and the linear λ-calculus have a long standing tradition in the study of natural language form and meaning. Among the proof calculi of linear logic, proof nets are of particular interest, offering an attractive geometric representation of derivations that is unburdened by the bureaucratic complications of conventional prooftheoretic formats. Building on recent advances in set-theoretic learning, we propose a neural variant of proof nets based on Sinkhorn networks, which allows us to translate parsing as the problem of extracting syntactic primitives and permuting them into alignment. Our methodology induces a batch-efficient, end-to-end differentiable architecture that actualizes a formally grounded yet highly efficient neuro-symbolic parser. We test our approach on ÆThel, a dataset of type-logical derivations for written Dutch, where it manages to correctly transcribe raw text sentences into proofs and terms of the linear λ-calculus with an accuracy of as high as 70%.
Anthology ID:
2020.conll-1.3
Volume:
Proceedings of the 24th Conference on Computational Natural Language Learning
Month:
November
Year:
2020
Address:
Online
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
26–40
Language:
URL:
https://aclanthology.org/2020.conll-1.3
DOI:
10.18653/v1/2020.conll-1.3
Bibkey:
Cite (ACL):
Konstantinos Kogkalidis, Michael Moortgat, and Richard Moot. 2020. Neural Proof Nets. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 26–40, Online. Association for Computational Linguistics.
Cite (Informal):
Neural Proof Nets (Kogkalidis et al., CoNLL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.conll-1.3.pdf
Code
 konstantinosKokos/neural-proof-nets
Data
aethel