Neural Sequence-to-grid Module for Learning Symbolic Rules
This addresses the challenge of symbolic reasoning for AI systems, offering a novel approach to improve generalization, though it appears incremental as it builds on existing neural network structures.
The paper tackled the problem of out-of-distribution generalization in symbolic reasoning tasks, such as arithmetic and program evaluation, by proposing a neural sequence-to-grid module as an input preprocessor, which enabled neural networks to achieve OOD generalization where state-of-the-art models failed.
Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning. In particular, even state-of-the-art neural networks fail to achieve \textit{out-of-distribution} (OOD) generalization of symbolic reasoning tasks, whereas humans can easily extend learned symbolic rules. To resolve this difficulty, we propose a neural sequence-to-grid (seq2grid) module, an input preprocessor that automatically segments and aligns an input sequence into a grid. As our module outputs a grid via a novel differentiable mapping, any neural network structure taking a grid input, such as ResNet or TextCNN, can be jointly trained with our module in an end-to-end fashion. Extensive experiments show that neural networks having our module as an input preprocessor achieve OOD generalization on various arithmetic and algorithmic problems including number sequence prediction problems, algebraic word problems, and computer program evaluation problems while other state-of-the-art sequence transduction models cannot. Moreover, we verify that our module enhances TextCNN to solve the bAbI QA tasks without external memory.