Learning Language Representations with Logical Inductive Bias

TL;DR

This paper introduces FOLNet, a neural architecture embedding logical inductive bias into language models, enhancing reasoning and transfer capabilities beyond traditional transformer models.

Contribution

We develop FOLNet, a novel neural network encoding logical inductive bias with learnable Horn clauses, improving reasoning and transfer in language representations.

Findings

FOLNet outperforms existing transformer-based models on language understanding tasks.

Neural logic operators in FOLNet can compose self-attention modules.

Logical inductive bias enhances transfer capabilities of language models.

Abstract

Transformer architectures have achieved great success in solving natural language tasks, which learn strong language representations from large-scale unlabeled texts. In this paper, we seek to go further beyond and explore a new logical inductive bias for better language representation learning. Logic reasoning is known as a formal methodology to reach answers from given knowledge and facts. Inspired by such a view, we develop a novel neural architecture named FOLNet (First-Order Logic Network), to encode this new inductive bias. We construct a set of neural logic operators as learnable Horn clauses, which are further forward-chained into a fully differentiable neural architecture (FOLNet). Interestingly, we find that the self-attention module in transformers can be composed by two of our neural logic operators, which probably explains their strong reasoning performance. Our proposed FOLNet has the same input and output interfaces as other pretrained models and thus could be pretrained/finetuned by using similar losses. It also allows FOLNet to be used in a plug-and-play manner when replacing other pretrained models. With our logical inductive bias, the same set of ``logic deduction skills'' learned through pretraining are expected to be equally capable of solving diverse downstream tasks. For this reason, FOLNet learns language representations that have much stronger transfer capabilities. Experimental results on several language understanding tasks show that our pretrained FOLNet model outperforms the existing strong transformer-based approaches.