Exploring End-to-End Differentiable Natural Logic Modeling

Yufei Feng, Zi’ou Zheng, Quan Liu, Michael Greenspan, Xiaodan Zhu

Abstract
We explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natural logic formalism while introducing subsymbolic vector representations and neural components. The proposed model adapts module networks to model natural logic operations, which is enhanced with a memory component to model contextual information. Experiments show that the proposed framework can effectively model monotonicity-based reasoning, compared to the baseline neural network models without built-in inductive bias for monotonicity-based reasoning. Our proposed model shows to be robust when transferred from upward to downward inference. We perform further analyses on the performance of the proposed model on aggregation, showing the effectiveness of the proposed subcomponents on helping achieve better intermediate aggregation performance.
Anthology ID:
2020.coling-main.101
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1172–1185
Language:
URL:
https://aclanthology.org/2020.coling-main.101
DOI:
10.18653/v1/2020.coling-main.101
Bibkey:
Cite (ACL):
Yufei Feng, Zi’ou Zheng, Quan Liu, Michael Greenspan, and Xiaodan Zhu. 2020. Exploring End-to-End Differentiable Natural Logic Modeling. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1172–1185, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Exploring End-to-End Differentiable Natural Logic Modeling (Feng et al., COLING 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.101.pdf
Code
 feng-yufei/Neural-Natural-Logic
Data
HELP, MED, SNLI
PDF Search Code