Exploring End-to-End Differentiable Natural Logic Modeling

TL;DR

This paper introduces a differentiable neural model integrating natural logic for improved reasoning, demonstrating robustness and effectiveness in monotonicity-based inference and aggregation tasks.

Contribution

It presents a novel end-to-end differentiable framework combining natural logic with neural networks, including module networks and memory for contextual reasoning.

Findings

Effective modeling of monotonicity-based reasoning

Robust transferability from upward to downward inference

Improved intermediate aggregation performance

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.