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Computer Science > Machine Learning

arXiv:2210.01603 (cs)
[Submitted on 4 Oct 2022]

Title:Neural-Symbolic Recursive Machine for Systematic Generalization

Authors:Qing Li, Yixin Zhu, Yitao Liang, Ying Nian Wu, Song-Chun Zhu,
Siyuan Huang
Download a PDF of the paper titled Neural-Symbolic Recursive Machine
for Systematic Generalization, by Qing Li and 5 other authors
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    Abstract:Despite the tremendous success, existing machine
    learning models still fall short of human-like systematic
    generalization -- learning compositional rules from limited data
    and applying them to unseen combinations in various domains. We
    propose Neural-Symbolic Recursive Machine (NSR) to tackle this
    deficiency. The core representation of NSR is a Grounded Symbol
    System (GSS) with combinatorial syntax and semantics, which
    entirely emerges from training data. Akin to the neuroscience
    studies suggesting separate brain systems for perceptual,
    syntactic, and semantic processing, NSR implements analogous
    separate modules of neural perception, syntactic parsing, and
    semantic reasoning, which are jointly learned by a
    deduction-abduction algorithm. We prove that NSR is expressive
    enough to model various sequence-to-sequence tasks. Superior
    systematic generalization is achieved via the inductive biases of
    equivariance and recursiveness embedded in NSR. In experiments,
    NSR achieves state-of-the-art performance in three benchmarks
    from different domains: SCAN for semantic parsing, PCFG for
    string manipulation, and HINT for arithmetic reasoning.
    Specifically, NSR achieves 100% generalization accuracy on SCAN
    and PCFG and outperforms state-of-the-art models on HINT by about
    23%. Our NSR demonstrates stronger generalization than pure
    neural networks due to its symbolic representation and inductive
    biases. NSR also demonstrates better transferability than
    existing neural-symbolic approaches due to less domain-specific
    knowledge required.

Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL);
          Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2210.01603 [cs.LG]
          (or arXiv:2210.01603v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2210.01603
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Qing Li [view email]
[v1] Tue, 4 Oct 2022 13:27:38 UTC (3,058 KB)
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