Exploring End-to-End Differentiable Natural Logic Modeling

By Yufei Feng, Zi'ou Zheng, QUAN LIU, Michael A. Greenspan, Xiao-Dan Zhu. 2020

15 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, methodology): “Natural Logic, a term originally coined by Lakoff (1972), has been previously employed in several information extraction and NLU tasks such as Natural Language Inference (NLI, Angeli and Manning, 2014; Abzianidze, 2017a; Feng et al., 2020), question answering (Angeli et al.”